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Open AccessEditorial

Statement of Peer Review

by Pavan Hiremath, Suhas Kowshik, Ritesh Bhat, Rajiv Selvam and Nithesh Naik

Eng. Proc. 2023, 59(1), 239; https://doi.org/10.3390/engproc2023059239 - 14 Mar 2024

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Abstract

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to a peer-review process administered by the volume editors [...] Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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3 pages, 1186 KB

Open AccessEditorial

Preface: International Conference on Recent Advances in Science and Engineering (RAiSE-2023)

by Pavan Hiremath, Suhas Kowshik, Ritesh Bhat, Rajiv Selvam and Nithesh Naik

Eng. Proc. 2023, 59(1), 240; https://doi.org/10.3390/engproc2023059240 - 15 Mar 2024

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Abstract

The International Conference on Recent Advances in Science and Engineering, RAiSE-2023, organized by the Department of Mechanical & Industrial Engineering at Manipal Institute of Technology, MAHE, Manipal, India, in collaboration with the School of Engineering and IT at MAHE Dubai, UAE, on 4 [...] Read more.

The International Conference on Recent Advances in Science and Engineering, RAiSE-2023, organized by the Department of Mechanical & Industrial Engineering at Manipal Institute of Technology, MAHE, Manipal, India, in collaboration with the School of Engineering and IT at MAHE Dubai, UAE, on 4 and 5 October 2023 in hybrid mode at MAHE Dubai was a significant milestone in the scientific and engineering community [...] Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1036 KB

Open AccessProceeding Paper

A Secure Framework for Communication and Data Processing in Web Applications

by Suprakash Sudarsanan Nair and Karuppasamy Mariappan

Eng. Proc. 2023, 59(1), 1; https://doi.org/10.3390/engproc2023059001 - 10 Dec 2023

Cited by 2 | Viewed by 3145

Abstract

Web applications are widely used, and the applications deployed on the web do not always satisfy all the security policies. This may arise due to less secure configurations, less knowledge in security configurations, or due to insecure coding practices. Even though a lot [...] Read more.

Web applications are widely used, and the applications deployed on the web do not always satisfy all the security policies. This may arise due to less secure configurations, less knowledge in security configurations, or due to insecure coding practices. Even though a lot of practices are available, a lot of security loopholes are still available for hackers to steal information. A secure web application framework is discussed here which incorporates solutions to major security loopholes that attackers may use for stealing information or compromising systems. The security framework proposed here ensures an encrypted data transfer making the data safe and server-side vulnerability detection and avoidance for major attacks like SQLinjection (SQLi) and Cross Site Scripting (XSS). The client side of the framework is responsible for validations, encryption, and session management through a JavaScript module. The server side of the framework is responsible for decryption and validation, data management, and URL management. The framework deployed with PHP showed a good outcome when tested with the Arachni web application security scanner. The framework will be further studied for performance with huge workloads. Further, the work will be extended to cover other attacks. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 473 KB

Open AccessProceeding Paper

A Comprehensive Analysis of the User Experience in Digital Platforms Concerning the Practice of Nudging User Behaviour

by Noel John Veigas, Ritik D. Shah, Dasharathraj K. Shetty, Tojo Thomas, Shreepathy Ranga Bhatta and Nikita Panwar

Eng. Proc. 2023, 59(1), 2; https://doi.org/10.3390/engproc2023059002 - 11 Dec 2023

Cited by 4 | Viewed by 7384

Abstract

This research paper unveils an all-encompassing literature exploration into “nudging” in digital platforms and its profound impact on the user experience. This study delved into various sources spanning academic research papers, corporate reports, books, and online publications, acquired through a thorough four-step approach. [...] Read more.

This research paper unveils an all-encompassing literature exploration into “nudging” in digital platforms and its profound impact on the user experience. This study delved into various sources spanning academic research papers, corporate reports, books, and online publications, acquired through a thorough four-step approach. The methodology entailed unearthing pertinent sources via diverse academic databases and industry networks, and a diligent review process to estimate their relevance and calibre. Data extraction from each selected source focused on the employed nudge techniques, underlying behavioural principles, and their repercussions on the user experience. The findings were subsequently synthesised to unearth the existing literature’s prevalent themes, disparities, and prospective gaps. The paper underscores the importance of nudging as a potent driver of user actions while safeguarding their autonomy. We employed a comprehensive approach to explore nudging application and influences on digital platforms, including academic database searches, corporate reports, and web blogs. We thoroughly extracted data on platform types, nudging strategies, behavioural theories, and user experience influences and impacts. Our study deliberates on potential future research trajectories, encompassing ethical considerations and personalised nudging methodologies. Ultimately, this study underscores how applying nudge techniques in the architecture of digital platforms can elevate user experiences and confer value upon both users and providers. However, the findings acknowledge the inherent limitations that accompany any literature review and may not encapsulate every facet of the subject matter. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 501 KB

Open AccessProceeding Paper

A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging

by Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan and Shreepathy Ranga Bhatta

Eng. Proc. 2023, 59(1), 3; https://doi.org/10.3390/engproc2023059003 - 11 Dec 2023

Cited by 21 | Viewed by 7970

Abstract

Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. CNNs are particularly adept in automatically identifying complex patterns and features in [...] Read more.

Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. CNNs are particularly adept in automatically identifying complex patterns and features in pictures like X-rays, CT scans, and MRIs. They accomplish this by capturing hierarchical information utilizing layers of convolutional and pooling processes. By enabling precise disease diagnosis, anatomical structure segmentation, and even patient outcomes’ prediction, CNNs have transformed medical imaging. In this review paper, we examine how crucial CNNs are for improving diagnostic effectiveness and efficiency across a range of medical imaging applications. This review details how Convolutional Neural Networks (CNNs) are used, focusing on the development and use of 3D CNNs for processing and categorizing multidimensional and moving images. The paper discusses how critical 3D CNNs are in areas like analyzing surveillance videos and, especially, in the field of medical imaging to find pathological tissues. With this method, pathologists can segment the layers of the bladder with a lot more accuracy, which cuts down on the time they have to spend looking over them by hand. CNNs use specific filters to find spatial and temporal relationships in images, making understanding and interpreting them easier. CNNs are better at fitting image datasets because they have fewer parameters and weights that can be used more than once. This makes the network better able to understand complex images. This thorough review shows how 3D CNNs could improve the speed and accuracy of processing and analyzing medical images and how far they have already come. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1997 KB

Open AccessProceeding Paper

Determination of the Fracture Point for Inconel-718 Using Luder’s Band Method

by Arupratan Gupta and Raghavendra C. Kamath

Eng. Proc. 2023, 59(1), 4; https://doi.org/10.3390/engproc2023059004 - 11 Dec 2023

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Abstract

The current scenario demands the usage of alternate materials with lower stress and stress–strain energy deformation for applications in gas turbines, chassis of automobiles, and biomedical instruments. The work on Inconel-718 can be carried out as it is a new material; it can [...] Read more.

The current scenario demands the usage of alternate materials with lower stress and stress–strain energy deformation for applications in gas turbines, chassis of automobiles, and biomedical instruments. The work on Inconel-718 can be carried out as it is a new material; it can be used for many applications in addition to its usage in automobiles. Inconel-718 is a superalloy of nickel (Ni) and chromium (Cr). Inconel-718 is corrosion resistant and oxidation resistant when subjected to extreme temperature conditions. But when applying tensile and compressive load, it bends, causing the formation of Luder’s band. The work analyses the formation of Luder’s band in Inconel-718. The methodology for detecting Luder’s band is based on the material structure. It also depends on the material’s rigidity modulus and the shear stress ratio to shear strain. The stress, harmonic, and thermal analyses were carried out using ANSYS to find the red-hot zone for the formation of Luder’s band. The results demonstrate that Luder’s band is mainly formed at the middle point of the frame. The stress for Inconel-718 is in the range of 0.064454 MPa to 81.514 MPa, whereas the frequency varies from 0.71157 to 475.87 Hz under vibration load. Conversely, while heating Inconel-718, the temperature varies from 1.6009 × 10⁵ to 112.83 °C. The analysis shows that Inconel-718 is a better material for designing automobile parts. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 633 KB

Open AccessProceeding Paper

A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks

by Anitha Christy Angelin and Salaja Silas

Eng. Proc. 2023, 59(1), 5; https://doi.org/10.3390/engproc2023059005 - 10 Dec 2023

Cited by 5 | Viewed by 2207

Abstract

In crucial applications, sensor node coverage of the objective zone must be stabilized in Wireless Sensor Networks (WSN). A network with holes in coverage is more susceptible to node failures or malicious attacks. According to the total number of hops used to transport [...] Read more.

In crucial applications, sensor node coverage of the objective zone must be stabilized in Wireless Sensor Networks (WSN). A network with holes in coverage is more susceptible to node failures or malicious attacks. According to the total number of hops used to transport data, nodes may calculate their distance from the sink node. A coverage hole may be present if a node notices a much higher hop count than its neighbors. The network becomes more robust and resilient to diverse problems by proactively recognizing and correcting coverage holes. Coverage hole identification aids in the efficient use of network resources. By identifying places with poor coverage, resources such as electricity and bandwidth may be efficiently deployed to increase coverage in specific areas or extend the network lifetime overall. However, some node sensors die while the network operates due to energy restrictions, which may disturb the inclusion of the objective zone, resulting in a coverage hole. Due to limited battery life, the existence of impediments and physical damage to sensor nodes, coverage holes may emerge in sensor networks. Early identification of coverage holes enables prompt maintenance and troubleshooting, which minimizes the need for future major and expensive replacements or reconfigurations. The loss on the region of interest may be calculated by locating the coverage holes and identifying the malfunctioning node that created it. This article discusses many coverage-hole-detecting methods, classification approaches, and different performance comparison assessments. Compared to conventional techniques for detecting coverage holes, the investigated methods contribute to the universal viewpoint on holes and compute the number of holes quite precisely. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 892 KB

Open AccessProceeding Paper

Biodegradability of Musa Acuminata (Banana)-Fiber-Reinforced Bio-Based Epoxy Composites: The Influence of Montmorillonite Clay

by Nithesh Naik, Ritesh Bhat, B. Shivamurthy, B.H.S. Thimmappa, Nagaraja Shetty and Yashaarth Kaushik

Eng. Proc. 2023, 59(1), 6; https://doi.org/10.3390/engproc2023059006 - 11 Dec 2023

Cited by 5 | Viewed by 3884

Abstract

The increasing environmental concerns associated with conventional composites, made using glass-fiber-reinforced polymers (GFRP) and carbon-fiber-reinforced polymers (CFRP), have shifted attention to bio-based composites. These environmentally responsible alternatives offer performance without sacrificing biodegradability. The present study examines the biodegradability of a novel bio-based epoxy [...] Read more.

The increasing environmental concerns associated with conventional composites, made using glass-fiber-reinforced polymers (GFRP) and carbon-fiber-reinforced polymers (CFRP), have shifted attention to bio-based composites. These environmentally responsible alternatives offer performance without sacrificing biodegradability. The present study examines the biodegradability of a novel bio-based epoxy composite reinforced with Musa acuminata (banana) fibers. Two composite variants were compared: one with 2.5% Montmorillonite (MMT) nanoclay and one without. While previous research has demonstrated an enhancement in mechanical and physical properties of polymer matrix composites with the addition of MMT nanoclay, it was hypothesized in this study that nanoclay addition would not significantly impact the composites’ biodegradability. To confirm this, we conducted standard biodegradability tests and an SEM analysis. The SEM results revealed a uniform distribution of MMT nanoclay within the bio-based polymer matrix, in addition to strong interfacial adhesion and decreased void crater sizes. The inclusion of nanoclay did not significantly impact the composites’ biodegradability, according to the statistical analysis provided in the present study. The present study also developed regression models to predict biodegradability over time to facilitate the determination of the timespan required for 100 percent biodegradability of the tested bio-based composite. Thus, this study is a significant benchmark for advancing eco-friendly composite materials. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1514 KB

Open AccessProceeding Paper

A Model of Gamification by Combining and Motivating E-Learners and Filtering Jobs for Candidates

by Sherin Eliyas and Ranjana P

Eng. Proc. 2023, 59(1), 7; https://doi.org/10.3390/engproc2023059007 - 10 Dec 2023

Cited by 2 | Viewed by 1577

Abstract

Early in the 1990s, recommender systems emerged to assist users in dealing with the cognitive overload caused by the internet. Since then, similar systems have expanded into many more capacities, such as assisting users in exploration, enhancing decision making, or even providing entertainment. [...] Read more.

Early in the 1990s, recommender systems emerged to assist users in dealing with the cognitive overload caused by the internet. Since then, similar systems have expanded into many more capacities, such as assisting users in exploration, enhancing decision making, or even providing entertainment. Understanding the user task and how to modify the advice to assist it are made possible by these features. Recommender systems for education have been proposed in related research. These recommender systems assist students in locating the learning materials that best suit their requirements. One of the primary requirements of the online social platform is to engage the user in an effective way. For this purpose, online media starts to use gamification to improve the user participants. The reward system for online media widely uses gamification elements such as points, badges, etc. Thereby, in a badge-based system, an unachieved badge highly influences the gamification system. In this paper, unachieved and achievable badges were recommended using item-based collaborative filtering recommendation model. This enables us to gather information from the candidates and make accurate predictions about the jobs that might suit them. This is also durable in the sense that any missing data about the candidate does not affect the algorithm as a whole as it is capable of making assumptions regarding the missing data based on similar data already stored in the database. Beyond this, this algorithm can be employed to host courses on the website. The empirical observation shows that the proposed model has recommended the badge with 70 percent accuracy. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1062 KB

Open AccessProceeding Paper

Geopositional Data Analysis Using Clustering Techniques to Assist Occupants in a Specific City

by Sneha George, Jayakumar Keirolona Safana Seles, Duraipandi Brindha, Theena Jemima Jebaseeli and Laya Vemulapalli

Eng. Proc. 2023, 59(1), 8; https://doi.org/10.3390/engproc2023059008 - 11 Dec 2023

Cited by 6 | Viewed by 2859

Abstract

Geolocation and Geographic Information Systems (GIS) are becoming essential tools in several sectors. Clustering-based geopositional data analysis has enormous potential for helping the citizens of a given city. The insights gained from this kind of study can assist inhabitants and tourists in making [...] Read more.

Geolocation and Geographic Information Systems (GIS) are becoming essential tools in several sectors. Clustering-based geopositional data analysis has enormous potential for helping the citizens of a given city. The insights gained from this kind of study can assist inhabitants and tourists in making better-educated decisions and improve overall quality of life by shedding light on numerous facets of the city’s infrastructure, services, and facilities. Due to its capacity to combine databases and display geographic data, GIS has proven important in a variety of industries. City planners and other stakeholders may learn a lot about the requirements of the city’s residents by clustering geopositional data. Making wise judgments based on this knowledge will raise the standard of living for everyone who lives, works, and visits the city. The purpose of this research is to use k-means clustering to identify the best houses to live in for immigrants according to their expectations, amenities, price, and proximity to the workplace or educational institution, and provide them with the best accommodation suggestions. After gathering the geolocational data of the city to which the immigrants have moved, the details will be cleaned and the data will be analyzed using different data pre-processing and data exploratory techniques. At last, the data will be clustered using the k-means clustering algorithm. It is computationally efficient and operates perfectly when clusters are spherical and comparable in size. It is essential to handle data privacy and security properly while working with geopositional data. The quality of life for those who live in cities can be improved by utilizing clustering algorithms to analyze geopositional data. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 639 KB

Open AccessProceeding Paper

A Methodical Review of Iridology-Based Computer-Aided Organ Status Assessment Techniques

by Suja Alphonse, Ramachandran Venkatesan and Theena Jemima Jebaseeli

Eng. Proc. 2023, 59(1), 9; https://doi.org/10.3390/engproc2023059009 - 11 Dec 2023

Cited by 1 | Viewed by 18127

Abstract

The pseudoscience known as iridology makes the unsubstantiated claim that it can identify medical disorders by examining the iris, the colored portion of the eye. Iridology does not provide a reliable means of diagnosis, and there is no scientific proof to back up [...] Read more.

The pseudoscience known as iridology makes the unsubstantiated claim that it can identify medical disorders by examining the iris, the colored portion of the eye. Iridology does not provide a reliable means of diagnosis, and there is no scientific proof to back up its claims. To find patterns that are connected to particular medical conditions, computerized iris analysis software may need to examine thousands of iris images. A method of iridology known as Computer-Aided Iridology (CAI) uses software to study the iris. CAI still is not a medically accepted diagnostic technique and is not any more trustworthy than conventional iridology. Applying technology in medical science had a great impact on diagnosing diseases. Decision making is the most critical task in computer-aided applications. Computer vision and deep learning make this task more accurate and are widely used in many applications, mainly in diagnosing diseases. The methodologies, data acquisition source, and volume of data used for both training and testing in the pre-diagnosis of human organs utilizing iris patterns are thoroughly studied. Understanding its limitations allows researchers to concentrate on creating and evaluating improvements in technology that could boost its accuracy and usefulness. Iridology has been considered as having no use for years and becomes effective when combined with technology. This study includes various technical factors used in iridology for the pre-diagnosing of diseases. Recognizing the limitations of iridology allows healthcare providers to avoid errors in diagnosis and prevent individuals from undergoing redundant procedures or therapies based solely on iridology assessments. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 1340 KB

Open AccessProceeding Paper

Early Detection of Alzheimer’s Disease: An Extensive Review of Advancements in Machine Learning Mechanisms Using an Ensemble and Deep Learning Technique

by Renjith Prabhavathi Neelakandan, Ramesh Kandasamy, Balasubramani Subbiyan and Mariya Anto Bennet

Eng. Proc. 2023, 59(1), 10; https://doi.org/10.3390/engproc2023059010 - 11 Dec 2023

Cited by 11 | Viewed by 2836

Abstract

Alzheimer’s disease (AD) is the most common form of dementia in senior individuals. It is a progressive neurological ailment that predominantly affects memory, cognition, and behavior. An early AD diagnosis is essential for effective disease management and timely intervention. Due to its complexity [...] Read more.

Alzheimer’s disease (AD) is the most common form of dementia in senior individuals. It is a progressive neurological ailment that predominantly affects memory, cognition, and behavior. An early AD diagnosis is essential for effective disease management and timely intervention. Due to its complexity and heterogeneity, AD is, however, difficult to diagnose precisely. This paper investigates the integration of disparate machine learning algorithms to improve AD diagnostic accuracy. The used dataset includes instances with missing values, which are effectively managed by employing appropriate imputation techniques. Several feature selection algorithms are applied to the dataset to determine the most relevant characteristics. Moreover, the Synthetic Minority Oversampling Technique (SMOTE) is employed to address class imbalance issues. The proposed system employs an Ensemble Classification algorithm, which integrates the outcomes of multiple predictive models to enhance diagnostic accuracy. The proposed method has superior disease prediction capabilities in comparison to existing methods. The experiment employs a robust AD dataset from the UCI machine learning repository. The findings of this study contribute significantly to the field of AD diagnoses and pave the way for more precise and efficient early detection strategies. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1308 KB

Open AccessProceeding Paper

Cloud Service Broker Using Ontology-Based System

by Neeraj Kumar Singh, Abhishek Jain, Shruti Arya, Pawan Bhambu, Tanya Shruti and Vipin Kumar Chaudhary

Eng. Proc. 2023, 59(1), 11; https://doi.org/10.3390/engproc2023059011 - 11 Dec 2023

Cited by 1 | Viewed by 1669

Abstract

Cloud computing offers more advantages to clients and associations regarding capital uses and working cost investment funds. This study gives an ontological model of the cloud fabricating space to help with the data trade between the cloud-producing assets. The ideas of the proposed [...] Read more.

Cloud computing offers more advantages to clients and associations regarding capital uses and working cost investment funds. This study gives an ontological model of the cloud fabricating space to help with the data trade between the cloud-producing assets. The ideas of the proposed model depend on a writing survey of models of the cloud and models of assembling. In the research article, the problem addressed is how cloud brokers are providing cloud services in an efficient way to cloud users. It is the main prologue to an ontology-based, process-situated, and specialist framework that is autonomous of a society that permits most associations to utilize it. The rising number of cloud providers, the nonappearance of interoperability, and the heterogeneity in current open cloud stages lead to the requirement for creative frameworks to track down the foremost fitting cloud resource plan as successfully and mechanized as may be anticipated. In this paper, we depicted the building arrangement of a cloud organization made of two agreeable modules. The Cloud Agency’s objective is to naturally secure assets from suppliers on the premise of SLA evaluation rules and find the foremost reasonable cloud supplier that fulfills users’ prerequisites, and the Semantic Motor’s objective is to make a rationalist depiction of assets based on users’ benefit prerequisites and a brokering framework. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1309 KB

Open AccessProceeding Paper

Resource Management Techniques for the Internet of Things, Edge, and Fog Computing Environments

by Koushik Chakraborty, Manmohan Sharma, Krishnaveni Kommuri, Voore Subrahmanyam, Pratap Patil and Manmohan Singh Yadav

Eng. Proc. 2023, 59(1), 12; https://doi.org/10.3390/engproc2023059012 - 11 Jul 2023

Cited by 2 | Viewed by 1529

Abstract

A speculative exhibit for distributed computing organizations is implied as a haze of mists joining different parceled mists into a solitary fluid mass for on-request tasks. Fundamentally put, the mist between clouds would ensure that a cloud could use resources outside of its [...] Read more.

A speculative exhibit for distributed computing organizations is implied as a haze of mists joining different parceled mists into a solitary fluid mass for on-request tasks. Fundamentally put, the mist between clouds would ensure that a cloud could use resources outside of its run using current understandings with other cloud benefit providers. Edge processing is a growing registering perspective that brings several frameworks and devices to or near the client. The edge consists in handling data closer to where they are being created, dealing with additional important rates and volumes and resulting in a more conspicuous activity drove happening in real time. These centers perform continuous planning of the data that they receive within a millisecond response time. The center points discontinuously send logical summary information to the cloud. An example of an edge computer is a smartphone connected to a cloud system. Haze computing is more like a “gateway” to insights and control over handling. A haze computer connects to multiple edge computers simultaneously, resulting in a specialized set of devices for more efficient data handling and capacity. There are cutoff points to the actual resources and the geographic reach of any cloud. A cloud cannot help its customers if all its computational and storage capacities are used up. An inter-cloud system addresses situations in which one cloud gains access to other clouds’ frameworks for computing, capacity, or other assets. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1111 KB

Open AccessProceeding Paper

A Comprehensive Review on Unsupervised Domain Adaptation for 3D Segmentation and Reconstruction in CT Urography Imaging

by Shreya, Sushanth, Dasharathraj K. Shetty, Shreepathy Ranga Bhatta and Nikita Panwar

Eng. Proc. 2023, 59(1), 13; https://doi.org/10.3390/engproc2023059013 - 11 Dec 2023

Cited by 1 | Viewed by 1938

Abstract

Computed tomography urography (CTU) is a specialized radiological procedure that produces finely detailed pictures of the urinary system, comprising the kidneys, ureters, and bladder, using computed tomography (CT) scans. This diagnostic procedure’s main goal is to assess disorders that impact these vital organs, [...] Read more.

Computed tomography urography (CTU) is a specialized radiological procedure that produces finely detailed pictures of the urinary system, comprising the kidneys, ureters, and bladder, using computed tomography (CT) scans. This diagnostic procedure’s main goal is to assess disorders that impact these vital organs, such as stones in the kidneys, tumors, UTIs, and morphological anomalies. CTU has benefits like the capacity to deliver a personalized therapeutic strategy via radiomics and artificial intelligence technologies, as well as extra knowledge about abdominal anatomy. This comprehensive article looks at how computed tomography urography (CTU) is used and how it can be changed to evaluate the urinary system, especially the kidneys, bladder, and ureters. The most important part of this review is the discussion on 3D kidney segmentation and reconstruction from urographic images, which has helped doctors a lot with the accurate diagnosis and planning of treatment for kidney diseases. Even though 3D convolution networks have been used a lot in medical picture segmentation, it can be hard to adapt them to clinical data from different modalities that have not been seen before. The review gives an in-depth look at the current research on how an unsupervised domain adaptation or translation method can be used with 2D networks, especially for accurate kidney segmentation in urographic images. Through this thorough study, we want to show how these techniques can be used in medical imaging and how they might change in the future. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1161 KB

Open AccessProceeding Paper

CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

by Vinaya Babu Maddala and Mooramreddy Sreedevi

Eng. Proc. 2023, 59(1), 14; https://doi.org/10.3390/engproc2023059014 - 11 Dec 2023

Viewed by 1210

Abstract

Mining technologies depend upon their outcomes, focusing only on certain data features within the database. They select only certain features related to the process from diverse integrated data resources and transform them into a form suitable for mining tasks. Different implementations of mining [...] Read more.

Mining technologies depend upon their outcomes, focusing only on certain data features within the database. They select only certain features related to the process from diverse integrated data resources and transform them into a form suitable for mining tasks. Different implementations of mining techniques run on data sources, which may be of considerable volume, to extract different knowledge outcomes suitable for various analyses and decision-making processes. The proposed study provides the design and development of the Clustering of Non-Associated Items set (CNAIS) within a transactional database. The development of the algorithm and its application to the data set are described and the results are noted. Comparisons with state-of-the-art methods show that CNAIS exhibits better performance. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1886 KB

Open AccessProceeding Paper

Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining

by Smitha Bidadi Anjan Prasad and Raja Praveen Kumar Nakka

Eng. Proc. 2023, 59(1), 15; https://doi.org/10.3390/engproc2023059015 - 11 Dec 2023

Cited by 2 | Viewed by 1882

Abstract

In the education domain, the significance of student feedback and other stakeholders for raising educational standards has received more attention in recent years. As a result, numerous instruments and strategies for obtaining student input and assessing faculty performance, as well as other facets [...] Read more.

In the education domain, the significance of student feedback and other stakeholders for raising educational standards has received more attention in recent years. As a result, numerous instruments and strategies for obtaining student input and assessing faculty performance, as well as other facets of education, have been developed. There are two main methods to collect feedback from students, as follows: the direct and indirect methods. In the direct method, feedback is collected by distributing a questionnaire and taking their responses. The limitation of this method is that the true experience of students is not revealed, and there is room for bias in the collection and assessment of such a questionnaire. To overcome this limitation, the indirect method can be followed where social media posts can be used to collect feedback from students as they are active on social media and use it to express their opinions as posts. To address the problem of the manual annotation of large volumes of data, this paper proposes a machine learning method that uses the sentiment 140 dataset as the training set to automate the process of annotations of tweets. The same method can be used to label any qualitative data. In total, 5000 tweets were scraped and considered for this study. Various pre-processing methods, including byte-order-mark removal, hashtag removal, stop word removal, and tokenization, were applied to the data. The term frequency-inverse document frequency (TF-IDF) trigrams technique was then used to process the cleaned data. The TF-IDF technique using trigrams captures negation for sentiment analysis. The vectorized data are then processed using various machine learning algorithms to classify the polarity of tweets. Performance parameters such as the F1-score, recall, accuracy, and precision are compared. With a 94.16% F1-score, 94% precision, 94% recall, and 95.16% accuracy, the Ridge Classifier performed better than the others. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 937 KB

Open AccessProceeding Paper

Using Artificial Intelligence Methods to Create a Chatbot for University Questions and Answers

by Krishnamurthy Ramalakshmi, David Jasmine David, Mariappan Selvarathi and Theena Jemima Jebaseeli

Eng. Proc. 2023, 59(1), 16; https://doi.org/10.3390/engproc2023059016 - 11 Dec 2023

Cited by 7 | Viewed by 3367

Abstract

A chatbot is a computer program that uses general rules and Artificial Intelligence techniques to simulate human conversation. This paper highlights the different scenarios of human-computer interaction and the journey it has gone through from evolution to evolvement to innovation to the development [...] Read more.

A chatbot is a computer program that uses general rules and Artificial Intelligence techniques to simulate human conversation. This paper highlights the different scenarios of human-computer interaction and the journey it has gone through from evolution to evolvement to innovation to the development of the technical era. Here, the main focus is on the ways humans interact with the computer and how it has changed day-to-day life and reduced human efforts in performing everyday activities. There is an impact of HCI (Human–Computer Interaction) on people and has consequences in the form of both advantages and disadvantages of this interaction. The various innovations and machines have given birth to human–computer interaction as well as technology interaction. The main objective is to style the interface amongst men as well with Personal Computers (PCs) as usual as the interface amid beings. The user can interact in this system using text or voice. As per way as interaction is concerned direct, indirect, and strategic interaction of humans with computers and the latest gadgets is possible. Dynamic intelligence makes it like real-time communication with an individual. It can handle the user request and offer relevant information that can be used as a friend one would seek for knowledge. The proposed system is developed using the Rasa of an open-source platform. Further, the article focuses on the features and role of chatbots in an educational context. High precision in sentence analysis is attained with the aid of the proposed method up to a 91% hit ratio. The hit rate for the similarity computation is high. The system can handle a broader variety of requests as a consequence of its ability to recognize many ways to phrase the same inquiry and map them to related results. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 2284 KB

Open AccessProceeding Paper

Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection

by Deepak Dudeja, Ajit Noonia, S. Lavanya, Vandana Sharma, Varun Kumar, Sumaiya Rehan and R. Ramkumar

Eng. Proc. 2023, 59(1), 17; https://doi.org/10.3390/engproc2023059017 - 11 Dec 2023

Cited by 4 | Viewed by 2646

Abstract

Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to the core concepts and principles of bagging decision [...] Read more.

Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to the core concepts and principles of bagging decision trees used for breast cancer diagnosis. In this article, three main algorithms, viz. linear regression (LR), decision tree (DT), and random forest, were used. The random forest method used bagging techniques for selecting data points, and feature optimization was also carried out. Through our experiments, it has been found that the results obtained with the bagging trees algorithm outperform the result obtained with the best decision tree parameters. A feature optimization scheme was also introduced in the selection of data points during the training phase, which effectively increased accuracy. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 714 KB

Open AccessProceeding Paper

Firefly Optimized Resource Control and Routing Stability in MANET

by Purushothaman Chandra Sekar, Pichaimuthu Rajasekar, Sundaram Suresh Kumar, Mittaplayam Arunchalam Manivasagam and Chellappan Swarnamma Subash Kumar

Eng. Proc. 2023, 59(1), 18; https://doi.org/10.3390/engproc2023059018 - 11 Dec 2023

Cited by 1 | Viewed by 1560

Abstract

A mobile adhoc network (MANET) is a network that comprises mobile devices positioned in various places functioning without any central administration. Routing in MANET plays a vital role when the data packet (DP) is sent from source to destination. In order to improve [...] Read more.

A mobile adhoc network (MANET) is a network that comprises mobile devices positioned in various places functioning without any central administration. Routing in MANET plays a vital role when the data packet (DP) is sent from source to destination. In order to improve the routing stability in MANET, resource utilization (i.e., energy and bandwidth) has to be controlled. An effective firefly resource-optimized routing (FFROR) technique controls resource utilization and improves routing stability during data packet (DP) transmission in MANET. Initially, in FFROR, the firefly resource optimization (FFRO) algorithm generates the population of fireflies (i.e., mobile nodes). It calculates the light intensity of every firefly based on objective functions (i.e., minimum energy consumption and minimum bandwidth utilization). The FFRO algorithm ranks fireflies according to the light intensity and finds the best resource-optimized mobile node (MN) to send the DP to the destination. This, in turn, helps in finding the resource-optimized mobile nodes and choosing the route path for sending the DP to the destination. The proposed FFROR technique uses the FFRO algorithm to increase routing stability and throughput. The simulation is carried out to analyze the performance of proposed FFROR techniques with parameters such as energy consumption, bandwidth availability, routing stability, and throughput. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1377 KB

Open AccessProceeding Paper

Recent Developments in Machine Learning Predictive Analytics for Disaster Resource Allocation

by Sunita Pachar, Deepak Dudeja, Neha Batra, Vinam Tomar, John Philip Bhimavarapu and Avadh Kishor Singh

Eng. Proc. 2023, 59(1), 19; https://doi.org/10.3390/engproc2023059019 - 11 Dec 2023

Cited by 9 | Viewed by 5076

Abstract

To be effective, evidence-driven disaster risk management (DRM) relies on a wide variety of data types, information sources, and models. Weather modeling, the rupture of earthquake fault lines, and the creation of dynamic urban exposure measures all require extensive data collection from a [...] Read more.

To be effective, evidence-driven disaster risk management (DRM) relies on a wide variety of data types, information sources, and models. Weather modeling, the rupture of earthquake fault lines, and the creation of dynamic urban exposure measures all require extensive data collection from a variety of sources in addition to complex science. There are various methodologies to utilize AI to recognize necessities and asset accessibility by the likes of Twitter; however, the foremost broadly recognized and exact strategies remain cloudy. Within the occurrence of a catastrophe, machine learning apparatuses for designating assets are required to instantly help those in need. This overview appears to be necessary for additional examination with respect to an assertion on endorsed methods for calculation to demonstrate assurance, benchmarking datasets, crisis word references, word embedding techniques, and evaluation methods. As fiascos of all sorts become more common, these devices have the potential to improve real-time crisis administration over all stages of a catastrophe. This study aims to provide readers, including data scientists, with a clear and uncomplicated reference on how disaster risk management systems can benefit from machine learning. There are numerous sources of information on this set of technologies, which are both complicated and constantly changing. The volume of sensor data that can be analyzed has increased exponentially because of enormous increases in computational speed and capacity over the past few decades. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1114 KB

Open AccessProceeding Paper

Agricultural Farm Production Model for Smart Crop Yield Recommendations Using Machine Learning Techniques

by Kandasamy Vidhya, Sneha George, Palanisamy Suresh, Duraipandi Brindha and Theena Jemima Jebaseeli

Eng. Proc. 2023, 59(1), 20; https://doi.org/10.3390/engproc2023059020 - 11 Dec 2023

Cited by 10 | Viewed by 5201

Abstract

Smart agricultural monitoring is the use of cutting-edge technology to manage all elements impacting plants and lowering crop yield quality. The main objective of smart crop monitoring and management is to guarantee farmers optimal productivity. Additionally, the market for worldwide smart crop management [...] Read more.

Smart agricultural monitoring is the use of cutting-edge technology to manage all elements impacting plants and lowering crop yield quality. The main objective of smart crop monitoring and management is to guarantee farmers optimal productivity. Additionally, the market for worldwide smart crop management is expanding continuously as a result of the rising need for smart agricultural techniques. Machine learning techniques have the potential to be utilized to provide intelligent agricultural yield suggestions that will assist farmers in increasing their crop yields and profitability. Machine learning algorithms are used to analyze massive collections containing previous yield statistics, meteorological data, soil data, and other parameters in order to discover patterns and associations that might be used to predict agricultural yields. The methodology used in this system is that the farmer must enter the details of conditions in the field. Once entered into the system, the data are analyzed. This predicts the state of environmental conditions and predicts the crop that is suitable under these situations to give a greater yield. A web application is also built here for the farmer to analyze the information regarding their crops and to generate relevant reports. To find better crops under various conditions, the k-nearest neighbor (KNN) technique is used. Finally, the farmer achieves better results based on the conditions in the field, enabling them to plant the crop that is appropriate to those conditions. The proposed system helps a huge number of farmers by using IoT (Internet of Things) devices and web applications for smart irrigation. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 509 KB

Open AccessProceeding Paper

Role of Computational Material Science in Improving the Properties of Piezoelectric Smart Materials: A Review

by Amith K. V. and Raghavendra C. Kamath

Eng. Proc. 2023, 59(1), 21; https://doi.org/10.3390/engproc2023059021 - 11 Dec 2023

Cited by 4 | Viewed by 3911

Abstract

Piezoelectric smart materials have gained significant attention in various technological applications due to their ability to convert mechanical energy into electrical energy and vice versa. These materials have diverse energy harvesting, sensing, actuation, and biomedical engineering applications. Research investigations on piezoelectric smart materials [...] Read more.

Piezoelectric smart materials have gained significant attention in various technological applications due to their ability to convert mechanical energy into electrical energy and vice versa. These materials have diverse energy harvesting, sensing, actuation, and biomedical engineering applications. Research investigations on piezoelectric smart materials encompass many areas, including material development, characterization, modeling, device design, and manufacturing techniques. Computational material science is crucial in advancing these materials’ understanding, design, and optimization. This research paper aims to provide an overview of the computational approaches employed in piezoelectric smart materials. The state-of-the-art computational techniques used for modeling piezoelectric materials are reviewed, and their applications in device design are explored along with performance optimization. This comprehensive review highlights the potential of computational material science in shaping the future of piezoelectric smart materials. It is observed that density functional theory and molecular dynamics are commonly used techniques. At the same time, finite element and phase field methods are employed for specific applications requiring continuum modeling or phase evolution simulations. Further exploration reveals that computational material science optimizes existing smart materials’ structural and compositional parameters through modeling and simulation. This improves properties such as enhanced performance, increased durability, and greater functionality. In addition, computational material science is employed to design and predict the properties of new piezoelectric materials by utilizing advanced modeling techniques, enabling the discovery and development of materials with tailored piezoelectric properties for specific applications. Recent research advancements in piezoelectric smart materials have contributed to developing materials with improved properties, advanced fabrication techniques, and expanded application possibilities. These advancements have paved the way for the realization of innovative devices and systems that harness the unique capabilities of piezoelectric materials. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 1442 KB

Open AccessProceeding Paper

Heuristic Exploration of Vital Parameters for Cash Transactions through Mobiles in the Coastal Hinterland of India

by Pradeep Kumar Shetty, Shiva H. C. Prasad, Raghavendra C. Kamath, Ankur Agarwal, Aman S. Kishan and Lavanya Mishra

Eng. Proc. 2023, 59(1), 22; https://doi.org/10.3390/engproc2023059022 - 11 Dec 2023

Cited by 1 | Viewed by 1398

Abstract

The people of India sought digital modes of payment during the demonetization period in India (2016); with the increasing growth of the internet, electronic commerce (e-commerce) websites have become imperative for securely accessing payment gateways, encouraging the growth of digital payment processes and [...] Read more.

The people of India sought digital modes of payment during the demonetization period in India (2016); with the increasing growth of the internet, electronic commerce (e-commerce) websites have become imperative for securely accessing payment gateways, encouraging the growth of digital payment processes and payment app development. During the pandemic, there was an exponential increase in mobile payments using smartphones. The usage of mobiles and their market penetration with government schemes such as ‘Digital India’ accelerated the use of mobile payments by a large percentage of customers in the coastal hinterland (Manipal) of India. This study aimed to analyze the critical factors influencing digital payments in the university town of Manipal. From the literature, 13 regressors were shortlisted, and their effect was measured against a behavioral intention to use mobile payments. A structured and validated questionnaire is used as a research tool for data collection that is analyzed using structural equation modeling. The structure equation modeling included using smart partial least squares (SPLS), in which path coefficients, t-statistics, and consistency tests were conducted. The investigation found that ease of use, social influence, perceived behavioral control, rewards and offers, credibility, compatibility, perceived cost, impact on the environment, and government schemes have a positive influence on m-payments. Social influence has a strong influence on m-payments and is a direct enabler of technology acceptance. The critical factors were identified by using smart PLS as being ease of use and social influence, which were identified as the critical factors concerning m-payments. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1264 KB

Open AccessProceeding Paper

Enhancing Virtual Experiences: A Holistic Approach to Immersive Special Effects

by Georgios Tsaramirsis, Oussama H. Hamid, Amany Mohammed, Zamhar Ismail and Princy Randhawa

Eng. Proc. 2023, 59(1), 23; https://doi.org/10.3390/engproc2023059023 - 8 Dec 2023

Viewed by 1727

Abstract

To create a more immersive experience, electronic content developers utilize hardware solutions that not only display images and produce sounds but also manipulate the viewer’s real environment. These devices can control visual effects like lighting variations and fog, emit scents, simulate liquid effects, [...] Read more.

To create a more immersive experience, electronic content developers utilize hardware solutions that not only display images and produce sounds but also manipulate the viewer’s real environment. These devices can control visual effects like lighting variations and fog, emit scents, simulate liquid effects, and provide vibration or locomotion sensations, such as moving the viewer’s chair. The goal is to emulate additional sensations for the viewers and engender the belief that they are truly present within the virtual environment. These devices are typically found in specially designed cinemas referred to as xD cinemas, such as 4D, 5D, 9D, etc., where each effect is treated as an additional dimension, enhancing the overall experience. Currently, all of these effects are triggered by timers. The system determines which effect to play based on timers. This approach is problematic, for it requires programming each device for each movie. In this research, we address this problem by introducing the idea of Special Effect Tags (SETs) that can be added in the subtitle files. The SETs aim to serve as a standard that will allow the various devices to know when each artificial phenomenon should be triggered. They are generic and can support infinite artificial phenomena, also known as dimensions. This paper introduces the idea of a common special effect framework and a generic architecture of a special effects player that is independent of any specific hardware solutions. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 2812 KB

Open AccessProceeding Paper

Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise

by Shantaram B. Nadkarni, G. S. Vijay and Raghavendra C. Kamath

Eng. Proc. 2023, 59(1), 24; https://doi.org/10.3390/engproc2023059024 - 12 Dec 2023

Cited by 38 | Viewed by 8605

Abstract

Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a need to predict airfoil noise. This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict [...] Read more.

Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a need to predict airfoil noise. This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict the scaled sound pressure using five different input features. Diverse Random Forest and Gradient Boost Models are tested with five-fold cross-validation. Their performance is assessed based on mean-squared error, coefficient of determination, training time, and standard deviation. The results show that the Extremely Randomized Trees algorithm exhibits the most superior performance with the highest Coefficient of Determination. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1561 KB

Open AccessProceeding Paper

Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning

by Jayasree R and Sheela Selvakumari

Eng. Proc. 2023, 59(1), 25; https://doi.org/10.3390/engproc2023059025 - 12 Dec 2023

Cited by 4 | Viewed by 3291

Abstract

The development of a knowledge- and information-based society can be aided by higher education. Through research and extension efforts, higher education institutions must perform a variety of functions, including building an intelligent human resource pool, gaining new skills, and creating new knowledge. As [...] Read more.

The development of a knowledge- and information-based society can be aided by higher education. Through research and extension efforts, higher education institutions must perform a variety of functions, including building an intelligent human resource pool, gaining new skills, and creating new knowledge. As a result, the development of skilled workers with the ability to think critically, creatively, and logically is the primary focus of higher education institutions. However, there are some significant obstacles in the way of offering quality education, such as how to identify low-performing students and their causes. Predicting student performance has become challenging as a result of the vast quantity of data in educational databases. The lack of a developed system for assessing and monitoring student achievement is also not being considered. There are primarily two causes for this kind of situation. Initially, there was inadequate study of the various prediction techniques to select the ones that would best predict students’ success in educational environments. The second is the lack of investigation into the courses. In this research work, efforts have been made to identify low-performing students through the proposed Back Propagation Neural Network for Student Performance Analysis (BPNN-SPA) model, which generates more accurate, efficient, and dependable results as compared to some of the existing techniques and models. The performance of the proposed model is compared with the Support Vector Machine and Random Decision algorithms and evaluated by four significant performance metrics, namely, sensitivity, specificity, accuracy, and the F-measure. Based on performance measures, the proposed BPNN-SPA achieved better accuracy than existing algorithms. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 6366 KB

Open AccessProceeding Paper

LS-Dyna Impact Modelling on Carbon Fibre Reinforced Polymers (CFRP) Composite Aircraft Panel with Various Impactors

by Gayathri Ravinath and Jims John Wessley

Eng. Proc. 2023, 59(1), 26; https://doi.org/10.3390/engproc2023059026 - 10 Dec 2023

Cited by 2 | Viewed by 3257

Abstract

In the aviation industry, the usage of composites is increasing because of their unique feature in terms of damage tolerance and high structural integrity. It inspires the researcher to focus on dynamic behaviour of subsonic aircraft hat-shaped CFRP composite panels using the LS-Dyna [...] Read more.

In the aviation industry, the usage of composites is increasing because of their unique feature in terms of damage tolerance and high structural integrity. It inspires the researcher to focus on dynamic behaviour of subsonic aircraft hat-shaped CFRP composite panels using the LS-Dyna tool to prove its excellent impact behaviour with the help of spherical, ogival and conical impactors. High-velocity impact simulations conducted in this research work duplicate the Foreign Object Damage on aircraft panels. The impact load locations are identified from the literature and notable damage features such as stresses, delamination length, internal energy absorbed, resultant force, delamination zone, resultant acceleration and resultant velocity are compared for the chosen impactor shapes. The elastic and plastic failure zones are displayed very clearly in the results to avoid any further damage in the future. All results help to understand the composite shell behaviour and different damage patterns. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1922 KB

Open AccessProceeding Paper

Insights and Implications: Unraveling Critical Factors in Resistance Spot Welding of Dissimilar Metals through SS 347 and DSS 2205 Welds

by Prabhakaran M., Jeyasimman D. and Varatharajulu M.

Eng. Proc. 2023, 59(1), 27; https://doi.org/10.3390/engproc2023059027 - 12 Dec 2023

Cited by 4 | Viewed by 2275

Abstract

This research focuses on analyzing the microstructural and mechanical characteristics of SS 347 and DSS 2205 stainless steel dissimilar welds. This is achieved by altering the weld parameters, welding current and heating cycle at three different levels each. In total, nine experimental trials [...] Read more.

This research focuses on analyzing the microstructural and mechanical characteristics of SS 347 and DSS 2205 stainless steel dissimilar welds. This is achieved by altering the weld parameters, welding current and heating cycle at three different levels each. In total, nine experimental trials were conducted and the welded sheets were applied to macrograph studies and a tensile shear test for analyzing the nugget quality and mechanical strength. The welded specimens were placed for observation under a scanning electron microscope (SEM) to observe the microstructure of the weldments. Specimen 9 was subjected to a microhardness test. The macrograph study revealed that the nugget size grows proportionally to the rise in the welding current and heating cycle. When the current exceeds 7.5 kA, the size of the nugget exceeds the threshold value of 4√t, where ‘t’ is the sheet metal thickness. The tensile shear test results clearly indicate that as the nugget size grows, the tensile force also rises. Sample 9 possesses a maximum tensile force of 18 kN and the mode of failure observed is influenced by the welding current and heating cycles. The failure mode of sample 9 was pulled out and the microhardness was maximum at the fusion zone with 320 HV. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1093 KB

Open AccessProceeding Paper

A Comprehensive Analysis of Fake News Detection Models: A Systematic Literature Review and Current Challenges

by Alok Mishra and Halima Sadia

Eng. Proc. 2023, 59(1), 28; https://doi.org/10.3390/engproc2023059028 - 12 Dec 2023

Cited by 12 | Viewed by 10988

Abstract

In today’s age of social networking, web news inconsistencies have become a pressing concern. These discrepancies can mislead individuals when making important purchase decisions. Despite the existing research in this area, there is a need for more empirical and rigorous investigation into the [...] Read more.

In today’s age of social networking, web news inconsistencies have become a pressing concern. These discrepancies can mislead individuals when making important purchase decisions. Despite the existing research in this area, there is a need for more empirical and rigorous investigation into the inconsistencies reported in reviews. False reporting and disinformation on social media platforms can significantly impact societal stability and peace. Fake news is frequently disseminated on social media and can easily influence and deceive populations and governments. Many researchers are working toward distinguishing fake news from genuine news on social media platforms. The practical and timely identification of fake news can help prevent its spread. Our study focuses on how machine learning and deep learning algorithms are used to detect fraudulent data. The most fundamental and practical techniques deployed over recent years are investigated, classified, and defined in numerous datasets in an extended review model. Additionally, simulation media and recorded indicators of performance are reviewed in detail. The review, as mentioned above, provides a comprehensive analysis of key research findings, delving into pertinent issues that may impact individuals in the academic and professional realms interested in augmenting the reliability of automated FND models. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1674 KB

Open AccessProceeding Paper

Progressive Reservation of Cloud Services Using Multi-Cloud Broker System

by P. Subramanian, B. Rajkumar, Sunita Pachar, Rama Krishna Yellapragada, Smaranika Mohapatra and Sweeti

Eng. Proc. 2023, 59(1), 29; https://doi.org/10.3390/engproc2023059029 - 11 Dec 2023

Viewed by 1530

Abstract

Cloud brokers play a crucial role in providing an effective service by utilizing cloud computing. The middleware known as cloud brokers aids in the provision of effective cloud services to cloud users. There are a lot of cloud brokers who offer cloud services [...] Read more.

Cloud brokers play a crucial role in providing an effective service by utilizing cloud computing. The middleware known as cloud brokers aids in the provision of effective cloud services to cloud users. There are a lot of cloud brokers who offer cloud services to cloud users on a reservation-in-advance basis so that they do not have to rush to use the cloud. A cloud organization agent is IT work and an arrangement of activities in which an organization or other component improves at least one cloud organization for the better and at least one buyer of that help through three basic employments: counting combination, joining, and customization trade. Since cloud innovation offers a Cloud Benefit Brokerage stage for them to run their delicate and basic operations, it has gotten to be exponentially acknowledged by businesses all over the world. A cloud organization provider has to provide a profitable strategy for restricting the induction to the distinctive components of the cloud that the board organizes. The clients should be able to safely construct, create, and spare their claim reports, and there should be a centralized database detailing information from different lives of clients. Utilizing the proposed methodology system, users are provided with the reserved services that best suit their needs. Effective services were the subject of this research paper. The proposed system performs best due to its complexity. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 1703 KB

Open AccessProceeding Paper

A Photovoltaic (PV)-Wind Hybrid Energy System Using an Improved Deep Neural Network (IDNN)-Based Voltage Source Controller for a Microgrid Environment

by Manimekalai Maradi Anthonymuthu Prakasam, Muthulakshmi Karuppaiyen and Gopinath Siddan

Eng. Proc. 2023, 59(1), 30; https://doi.org/10.3390/engproc2023059030 - 12 Dec 2023

Cited by 6 | Viewed by 2107

Abstract

Presently, there has been a huge rise in the demand for power owing to increases in population and commercial organizations. Traditional power plants are not able to keep up with the increasing needs of customers. Finding a different way to meet consumers’ needs [...] Read more.

Presently, there has been a huge rise in the demand for power owing to increases in population and commercial organizations. Traditional power plants are not able to keep up with the increasing needs of customers. Finding a different way to meet consumers’ needs is the main problem in the current situation. Most RESs (renewable energy sources) like wind, solar, hydro/water sources and fuel cells are environmentally beneficial. The number of available resources has no bearing on how much electricity can be produced using RESs. Due to differences in natural resources, there are constant fluctuations in the availability of RESs. In this technical study, two significant RE (Renewable Energy) power sources—PV (photovoltaic) cells and WES (wind energy systems)—are studied in various weather scenarios. First, a cutting-edge intelligent controller system was created, which aids in tracking the peak power point. Due to the unpredictable nature of weather, a MPPT (maximum power point tracking) controller is required for RES. This work aims to present IDNN- (improved deep neural network) and MPPT-based unique methods for power generation using solar and winds. When a hybrid PV/WES system is integrated into MG s(microgrids), power quality may be improved and THD values can be reduced. It was confirmed from the results of the simulation that the proposed IDNN system yields better performance in different operating situations by means of lower MSE (mean square error) rates, lower THD (total harmonic distortion) and lower computational complexity than the existing method. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 2325 KB

Open AccessProceeding Paper

A Secure Lightweight Cryptographic Algorithm for the Internet of Things (IoT) Based on Deoxyribonucleic Acid (DNA) Sequences

by Archana S Nadhan and Jeena Jacob I

Eng. Proc. 2023, 59(1), 31; https://doi.org/10.3390/engproc2023059031 - 12 Dec 2023

Cited by 3 | Viewed by 1838

Abstract

The widespread adoption of the Internet of Things (IoT) across various domains has ushered numerous applications into our daily lives. Ensuring the security of sensitive data, including wirelessly transmitted private information and images generated by IoT devices is paramount. However, IoT devices are [...] Read more.

The widespread adoption of the Internet of Things (IoT) across various domains has ushered numerous applications into our daily lives. Ensuring the security of sensitive data, including wirelessly transmitted private information and images generated by IoT devices is paramount. However, IoT devices are often termed “constraint devices” due to their limited computational resources like CPU power or memory capacity. Also, ensuring the integrity of IoT devices and networks is imperative in fostering trust in the capabilities and benefits of IoT technology. Addressing data tampering, device vulnerabilities, and network weaknesses through proactive security measures is essential in realizing the full potential of the IoT while safeguarding against potential risks and disruptions. Traditional encryption approaches prove inadequate, as they demand excessive computational power; this is a challenge for IoT devices. To address this, a novel and less intrusive encryption method has been proposed, leveraging the inherent unpredictability of DNA nucleotide sequences. This approach is tailored to accommodate the resource constraints of IoT devices. By harnessing the intrinsic randomness of DNA sequences, a robust secret key is generated, significantly bolstering resilience against attackers. The key is crafted through uncomplicated substitution techniques and transposition operations. Upon satisfying the computational requisites of IoT devices and safeguarding image security, a DNA-based key comes into play for photo encryption. Rigorous testing has demonstrated its effectiveness, showcasing its superior attributes in terms of key size, encryption speed, and distortion minimization when compared to alternative encryption techniques. This innovative encryption paradigm not only upholds the integrity of IoT-generated data but does so without overwhelming the devices’ limited computing capabilities. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 2216 KB

Open AccessProceeding Paper

Effect of Lanthanum Doping on the Structural, Morphological, and Optical Properties of Spray-Coated ZnO Thin Films

by Manu Srivathsa and Bharathipura Venkataramana Rajendra

Eng. Proc. 2023, 59(1), 32; https://doi.org/10.3390/engproc2023059032 - 12 Dec 2023

Cited by 7 | Viewed by 2514

Abstract

In recent years, transparent conducting oxide semiconductor materials have found applications in both science and technology, especially in the areas of semiconductors, optoelectronics, and a wide range of energy efficiency devices. These TCO materials are the building blocks of various optoelectronic devices, such [...] Read more.

In recent years, transparent conducting oxide semiconductor materials have found applications in both science and technology, especially in the areas of semiconductors, optoelectronics, and a wide range of energy efficiency devices. These TCO materials are the building blocks of various optoelectronic devices, such as transparent thin-film transistors, solar cells, and light-emitting diodes. This work concentrates on the structure, morphology, and optical properties of ZnO and Zn_0.95La_0.05O thin films at 673 K using a chemical spray technique. The polycrystalline nature and wurtzite structure of ZnO were confirmed by using XRD analysis with preferred growth along the (1 0 1) plane. The Zn_0.95La_0.05O deposits showed maximum crystallinity of 15.4 nm and a strain value of 2.4 × 10⁻³. The lattice constants increased for lanthanum-doped ZnO thin films due to the ionic radii mismatch of the doping material, which causes lattice expansion. Fibrous morphology was observed for ZnO, and a mixed structure of grains and fibers was observed for Zn_0.95La_0.05O films, which confirms the insertion of La³⁺ into the Zn²⁺ position. The Zn_0.95La_0.05O deposits showed transmittance above 80% due to the increased crystalline quality and a bandgap of 3.32 eV. The photoluminescence spectra showed peaks corresponding to e-h recombination, zinc defects (Zn_i and O_zn), and oxygen vacancy (O_i and V_o). The lanthanum-doped ZnO films showed increased band-edge emission and decreased defect-related peaks due to the increased crystalline quality. Hence, the doping of La³⁺ ions into a ZnO lattice enhances the crystalline quality and increases the transparency of the host ZnO matrix, which is suitable for optoelectric device applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1180 KB

Open AccessProceeding Paper

Market-Inspired Framework for Securing Internet of Things Computing Environment

by Sunita Pachar, Neeraj Kumar Singh, Nazeer Shaik, Shruti Arya, John Philip Bhimavarapu and Sunil Kumar Vishwakarma

Eng. Proc. 2023, 59(1), 33; https://doi.org/10.3390/engproc2023059033 - 12 Dec 2023

Viewed by 1436

Abstract

IoT security, also known as Internet of Things security, is an innovation component that focuses on protecting connected devices and systems on the Internet of Things (IoT). There are several fields which relate to the IoT framework such as computers, mechanical and computerized [...] Read more.

IoT security, also known as Internet of Things security, is an innovation component that focuses on protecting connected devices and systems on the Internet of Things (IoT). There are several fields which relate to the IoT framework such as computers, mechanical and computerized machines, objects, creatures, and people. Each thing has a unique identifier and the ability to transfer data across an organization. The Internet of Things organizations help to obtain a practical advantage by taking care of the hardships of consolidating wearables, sensors, associations, cloud, and applications without choosing security. Development is stressed over partner contraptions with each other to work with the correspondence between them. The devices that are related will really need to share the information that can be used as a commitment by any contraption that is dependent upon various contraptions for input. It is known as the Trap of Things, like the Internet. This development requires certifications to sort out among contraptions. Different industry-unequivocal data and IoT development expertise cover firmware improvement, transportability, conveyed registering, and data assessment, for making the market space an impressive range for end clients. The end clients receive soft assembled decisions concerning solid data assessment in IoT organizations. Nowadays, many IoT applications, computations, and organizations are utilizing services over the Internet. These are the most important applications that need security from the cyber web. If cyberattacks are going on in IoT devices, security is a must for the end users. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2147 KB

Open AccessProceeding Paper

Identification of Turmeric Rhizomes Using Image Processing and Machine Learning

by Shubhangi Patil and Gouri Patil

Eng. Proc. 2023, 59(1), 34; https://doi.org/10.3390/engproc2023059034 - 12 Dec 2023

Cited by 6 | Viewed by 2891

Abstract

India is the world’s leading producer and exporter of turmeric. Indian turmeric is known as the best in the world because of its natural medicinal properties. Different turmeric varieties have different amounts of nutritional value, which results in variations in their cost and [...] Read more.

India is the world’s leading producer and exporter of turmeric. Indian turmeric is known as the best in the world because of its natural medicinal properties. Different turmeric varieties have different amounts of nutritional value, which results in variations in their cost and quality. The quality assessment of turmeric aids in evaluating and determining its quality, and it helps to promote its marketing. Hence, the identification of turmeric cultivars is of great importance. But it requires manual inspection by human experts, generates subjective results and is time-consuming. Machine vision will provide a more accurate and faster way to identify different agricultural products and their varieties. This study presents an automated system to identify turmeric rhizome varieties by extracting morphological, color and texture features. The classification of different rhizome types is carried out by using image processing techniques followed by K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and Linear Discriminant Analysis (LDA) classifiers. The proposed work shows promising results for the identification of turmeric rhizome varieties. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 1675 KB

Open AccessProceeding Paper

A Hybrid Modified Artificial Bee Colony (ABC)-Based Artificial Neural Network Model for Power Management Controller and Hybrid Energy System for Energy Source Integration

by Udayakumar Ramanathan, Sugumar Rajendran, Devi Thiyagarajan and Elankavi Rajendran

Eng. Proc. 2023, 59(1), 35; https://doi.org/10.3390/engproc2023059035 - 12 Dec 2023

Cited by 3 | Viewed by 1544

Abstract

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES [...] Read more.

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1897 KB

Open AccessProceeding Paper

Internet of Things Enabled Machine Learning-Based Smart Systems: A Bird’s Eye View

by Ashish Kumar Rastogi, Swapnesh Taterh and Billakurthi Suresh Kumar

Eng. Proc. 2023, 59(1), 36; https://doi.org/10.3390/engproc2023059036 - 12 Dec 2023

Cited by 3 | Viewed by 2827

Abstract

Machine learning (ML) helps the Internet of Things (IoT) become widely used by automatically identifying data patterns and extracting important insights from the vast pool of observed data. To efficiently serve corporations, governments, and individual consumers, the Internet of Things (IoT) needs machine [...] Read more.

Machine learning (ML) helps the Internet of Things (IoT) become widely used by automatically identifying data patterns and extracting important insights from the vast pool of observed data. To efficiently serve corporations, governments, and individual consumers, the Internet of Things (IoT) needs machine learning (ML). The IoT gathers environmental data and automates decision-making using sophisticated methods based on human judgement. Data, application, and industry perspectives are used to organise and assess machine learning–IoT literature. We discuss how machine learning and the Internet of Things can make our surroundings smarter by reviewing relevant research. Our analysis includes many cutting-edge methods. We also discuss pandemic control, networked-enabled cars, distributed computing, trivial deep learning, and the Internet of Things. Technological, personal, commercial, and societal concerns face the Internet of Things. Learning how to use the IoT can improve society’s well-being and longevity. We also examine a case study to find comparative results among various machine learning methods integrated with the IoT. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1478 KB

Open AccessProceeding Paper

Human Emotion Detection Using DeepFace and Artificial Intelligence

by Ramachandran Venkatesan, Sundarsingh Shirly, Mariappan Selvarathi and Theena Jemima Jebaseeli

Eng. Proc. 2023, 59(1), 37; https://doi.org/10.3390/engproc2023059037 - 12 Dec 2023

Cited by 27 | Viewed by 15605

Abstract

An emerging topic that has the potential to enhance user experience, reduce crime, and target advertising is human emotion recognition, utilizing DeepFace and Artificial Intelligence (AI). The same feeling may be expressed differently by many individuals. Accurately identifying emotions can be challenging, in [...] Read more.

An emerging topic that has the potential to enhance user experience, reduce crime, and target advertising is human emotion recognition, utilizing DeepFace and Artificial Intelligence (AI). The same feeling may be expressed differently by many individuals. Accurately identifying emotions can be challenging, in light of this. It helps to understand an emotion’s significance by looking at the context in which it is presented. Depending on the application, one must decide which AI technology to employ for detecting human emotions. Because of things like lighting and occlusion, using it in real-world situations can be difficult. Not every human emotion can be accurately detected by technology. Human–machine interaction technology is becoming more popular, and machines must comprehend human movements and expressions. When a machine recognizes human emotions, it gains a greater understanding of human behavior and increases the effectiveness of work. Text, audio, linguistic, and facial movements may all convey emotions. Facial expressions are important in determining a person’s emotions. There has been little research undertaken on the topic of real-time emotion identification, utilizing face photos and emotions. Using an Artificial Intelligence-based DeepFace approach, the proposed method recognizes real-time feelings from facial images and live emotions of persons. The proposed module extracts the facial features from an active shape DeepFace model by identifying 26 facial points to recognize human emotions. This approach recognizes the emotions of frustration, dissatisfaction, happiness, neutrality, and wonder. The proposed technology is unique, in that it implements emotion identification in real-time, with an average accuracy of 94% acquired from actual human emotions. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 5047 KB

Open AccessProceeding Paper

Using Chemical Precipitation to Recover Struvite from Household Wastewater for Agricultural Fertilizer Utilization

by Reya Issac, Muthukumar Lakshmi Prabha, Robinson Emilin Renitta, Sevanan Murugan, Jincy Ann George, Theena Jemima Jebaseeli and Subramanium Vijayanand

Eng. Proc. 2023, 59(1), 38; https://doi.org/10.3390/engproc2023059038 - 12 Dec 2023

Cited by 4 | Viewed by 4113

Abstract

Struvite is a substance that can be extracted from wastewater and has the potential to replace conventionally manufactured fertilizers and reduce environmental issues. A slow-release fertilizer can more effectively be used by matching the nutrient requirements of plants through the growing period and [...] Read more.

Struvite is a substance that can be extracted from wastewater and has the potential to replace conventionally manufactured fertilizers and reduce environmental issues. A slow-release fertilizer can more effectively be used by matching the nutrient requirements of plants through the growing period and gradually supplying N and P for crop growth. Struvite is an ecologically friendly fertilizer because of its gradual fertilizer treatment and high quality. Existing research indicates that the solubility and absorption of struvite by plants are equivalent to those of artificial phosphorus fertilizers such as triple superphosphate or potassium phosphate. Struvite is recognized to be an effective fertilizer for grass, tree seedlings, ornamental plants, vegetables, and flower beds. Struvite precipitation removes phosphorus and nitrogen from sewage water, hence alleviating phosphorus shortages from non-renewable phosphorus sources and water eutrophication. Struvite would also be useful in the grasslands and woods where fertilizers are used. However, the agricultural utility of struvite has not been thoroughly investigated. As a result, this work is reported as a pot experiment designed to assess the fertilizer value of struvite. Experimental settings were created, and pot experiments were conducted to establish the optimal amount of struvite based on two factors. The initial pH for struvite synthesis was 9. The formulated struvite fertilizers were compared to standard phosphorus fertilizers in the pot trials. Fourier-transform spectroscopy and Scanning Electron Microscopy (SEM) with Energy-Dispersive X Ray Spectroscopy (EDAX) were employed to support the quantitative findings. To summarize, struvite precipitation is a desirable and effective method for removing phosphate and nitrogen from domestic sewage water and using them as fertilizers. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 920 KB

Open AccessProceeding Paper

Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach

by Smitha Padshetty and Ambika

Eng. Proc. 2023, 59(1), 39; https://doi.org/10.3390/engproc2023059039 - 12 Dec 2023

Cited by 20 | Viewed by 5635

Abstract

Plant diseases can result in significant yield losses, posing a threat to food security and economic stability. Deep neural networks, particularly Convolutional Neural Networks (CNNs), have shown exceptional success in image classification tasks, often surpassing human-level performance. However, conventional methods for leaf disease [...] Read more.

Plant diseases can result in significant yield losses, posing a threat to food security and economic stability. Deep neural networks, particularly Convolutional Neural Networks (CNNs), have shown exceptional success in image classification tasks, often surpassing human-level performance. However, conventional methods for leaf disease detection relied on manual inspection by agricultural experts, leading to limited scalability and precision. To tackle these challenges, this research introduces a novel approach called the Leaky Rectilinear Residual Network (LRRN) for plant leaf disease detection. The LRRN model comprises three key modules—data pre-processing, feature extraction, and classification. It integrates ResNet architecture with the Leaky ReLU activation function to classify plant diseases. Experimental evaluations were performed on affected plant leaf disease images from the Plant Village dataset, utilizing performance evaluation metrics to assess the proposed model. The achieved results were compared to state-of-the-art techniques, demonstrating superior accuracy (94.56%), precision (93.48%), F1-scores (92.83%), recall (93.12%), and specificity (92.58%). These findings substantiate the effectiveness of the proposed LRRN method of plant leaf disease detection. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2046 KB

Open AccessProceeding Paper

Selecting a Suitable Flat in a High-Rise Apartment by Evaluation of Heat, Light, and Ventilation

by Aniket Bansal, Rohan Dinesh Horabyle, B. R. K. Holla and Arya Rajiv Lotliker

Eng. Proc. 2023, 59(1), 40; https://doi.org/10.3390/engproc2023059040 - 13 Dec 2023

Viewed by 2931

Abstract

In scientific literature, the impacts of heat, light, and ventilation on indoor settings have been extensively studied. It shows how important it is to consider a building’s HLV characteristics in the context of its surroundings. These elements have a direct impact on a [...] Read more.

In scientific literature, the impacts of heat, light, and ventilation on indoor settings have been extensively studied. It shows how important it is to consider a building’s HLV characteristics in the context of its surroundings. These elements have a direct impact on a building’s comfort level, energy effectiveness, and general sustainability. Many studies have investigated the effects of heat, light, and ventilation individually, rather than in combination with each other. This is because these factors have complex and dynamic interactions with each other, making it challenging to study them comprehensively. However, not many studies in this area have been made considering Indian geographical conditions. It can be challenging for a customer to find an apartment in a high rise building that meets their needs. Thus, using DesignBuilder tools at four different locations in India, a simulation was made and an analysis on the effects of HLV was performed for a symmetrical 10-storey building with adjacent buildings. An in-depth discussion of the air change rate of the building, daylighting performance in relation to different floors, and the difference between the indoor and outdoor temperatures of the building has been performed in this study. The criteria for choosing an apartment in a high rise building in accordance with the client’s requirements have also been derived from these results. Analysis on the effect of heat shows that the higher-density and taller surrounding buildings have a more pronounced effect on reducing the temperature difference. In the analysis of light, the height and distance of the surrounding buildings play a significant role in casting shadows on the main building. Ventilation analysis showed that higher floors have better ventilation compared to the lower floors and an increase in distance of the surrounding building increases the air change rate. The energy consumption analysis highlights that when the main building is surrounded by multiple buildings, energy consumption tends to decrease. The results indicate that as the building distance increases, energy consumption increases. Similar patterns are shown in all of the locations which were simulated, but the energy consumption load depends on the climatic condition of each location. Ahmedabad has the highest energy consumption load followed by Delhi, Guwahati, and Bangalore, irrespective of the distance and height of the surrounding buildings from the main building. Based on these findings, the guidelines were drawn for the selection of a suitable flat based on the requirement of the customer. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 2390 KB

Open AccessProceeding Paper

Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX)

by Ambidi Naveena and Udataneni Divya

Eng. Proc. 2023, 59(1), 41; https://doi.org/10.3390/engproc2023059041 - 11 Dec 2023

Cited by 1 | Viewed by 1588

Abstract

High data transmission rates over wide regions and to clients in locations where broadband service is not accessible are provided by WiMAX, based on IEEE 802.16 standards for Broadband Wireless Access (BWA). The use of several antennas for sending and receiving data is [...] Read more.

High data transmission rates over wide regions and to clients in locations where broadband service is not accessible are provided by WiMAX, based on IEEE 802.16 standards for Broadband Wireless Access (BWA). The use of several antennas for sending and receiving data is a common feature of MIMO systems in wireless communications. WiMAX-MIMO devices are designed to improve WiMAX system performance. An analysis of MIMO-WiMAX systems using various modulations and coding rates in a Rayleigh fading channel is presented in this work. Matlab software version (R2018a) is used to examine the relationship between bit error rates and signal-to-noise ratios with various cyclic prefixes and single/multiple transceivers. The codes of Alamouti STBC are used to examine the BER performance of MIMO-WiMAX. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 2565 KB

Open AccessProceeding Paper

Reducing Equipment Failure Risks by Redesigning of Products and Processes

by Ashweni Jain, Niranjan Parkhi and Prafulla Wankhade

Eng. Proc. 2023, 59(1), 42; https://doi.org/10.3390/engproc2023059042 - 13 Dec 2023

Viewed by 1493

Abstract

Low-voltage (LV) network assets, although they do not play a significant role in reliability indices compared to medium-voltage (MV) assets like the transformer and switchgears, are required to be designed in a way that would mitigate the risk of sporadic failures, hence incurring [...] Read more.

Low-voltage (LV) network assets, although they do not play a significant role in reliability indices compared to medium-voltage (MV) assets like the transformer and switchgears, are required to be designed in a way that would mitigate the risk of sporadic failures, hence incurring an R&M cost. LV assets like LV cables, distribution panels, molded-case circuit breakers (MCCBs), and miniature circuit breakers (MCBs) generally do not have a planned maintenance (PM) schedule and are procured based on the run-to-failure concept in view of the huge volume. These assets are exposed to the harshest of environmental and operation conditions. Hence, it is imperative that we take the necessary measures during the design stage such that they are able to cater to their stringent duties, which include frequent short circuits, exposure to the environment, and thermal overloads. It is also important to periodically review the product design based on site feedback and product performance to re-calibrate the product and its associated processes. Through this technical paper, several case studies are presented wherein special terminal connectors with shear bolts were designed to mitigate the thermal hotspot issues causing frequent fire and failures—i.e., vertical fuse switch disconnectors (VFSDs) and miniature circuit breaker (MCBs). A case study on condition monitoring through a substation inspection schedule is also presented, through which potential failures were averted in time. The observations and measurements are mapped in an SAP system for trend analysis. With the adoption of effective product and process design, AEML has reduced asset failures. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 749 KB

Open AccessProceeding Paper

Sales-Based Models for Resource Management and Scheduling in Artificial Intelligence Systems

by Deepak Dudeja, Shweta Mayor Sabharwal, Yatish Ganganwar, Manoj Singhal, Nitin Goyal and Ashish Tiwari

Eng. Proc. 2023, 59(1), 43; https://doi.org/10.3390/engproc2023059043 - 13 Dec 2023

Cited by 32 | Viewed by 2053

Abstract

Recent trends have shown a greatly increasing number of users in the digital world, so there is a need for a large number of resources. To handle these resources, there is the need to manage and schedule in an optimized manner using artificial [...] Read more.

Recent trends have shown a greatly increasing number of users in the digital world, so there is a need for a large number of resources. To handle these resources, there is the need to manage and schedule in an optimized manner using artificial intelligence (AI) systems. These systems deal with the business-common method of managing offerings. Ordinary models consolidate inbound deals, outbound bargains, account-based offerings, or a mix of diverse models. An organization model may gather multiple choices that an organization makes over a long period of time, considering a system, cycle, or trade. In our approach, computational resources are treated as commodities that can be bought and sold in a decentralized marketplace. Agents representing AI tasks or workloads participate in resource auctions, competing for the resources they need. The allocation of resources is determined through competitive bidding, where the highest bidder secures the required resources. This approach encourages efficient resource utilization and fair distribution based on the tasks’ priorities and value. Our sales-based models for resource management and scheduling offer a promising solution for optimizing AI systems’ resource allocation. By applying principles from auction theory and market dynamics, AI systems can become more adaptive, responsive, and efficient in managing computational resources, ultimately leading to improved performance and resource utilization. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1309 KB

Open AccessProceeding Paper

Future Fusion+ UNet (R2U-Net) Deep Learning Architecture for Breast Mass Segmentation

by Shruthishree Surendrarao Honnahalli, Harshvardhan Tiwari and Devaraj Verma Chitragar

Eng. Proc. 2023, 59(1), 44; https://doi.org/10.3390/engproc2023059044 - 11 Dec 2023

Cited by 9 | Viewed by 2515

Abstract

R2U-Net, or Recurrent Residual U-Net, is a U-Net extension that includes both residual and recurrent connections for image segmentation tasks. R2U-Net is an image segmentation task-focused network that mixes residual and recurrent connections to boost performance and manage sequential data. Semantic segmentation algorithms [...] Read more.

R2U-Net, or Recurrent Residual U-Net, is a U-Net extension that includes both residual and recurrent connections for image segmentation tasks. R2U-Net is an image segmentation task-focused network that mixes residual and recurrent connections to boost performance and manage sequential data. Semantic segmentation algorithms based on deep learning (DL) have demonstrated state-of-the-art performance recently. Specifically, these methods have proven effective for tasks like medical image segmentation, classification, and detection. U-Net is one of the most prominent deep learning techniques for these applications. These proposed structures for segmentation problems have various advantages. In addition, better feature representation for segmentation tasks is provided by accumulating features using recurrent residual convolutional layers. Moreover allows us to design a more effective U-Net architecture for medical picture segmentation using the same amount of network parameters. The experimental results reveal that the model outperforms analogous models such as R2U-Net on segmentation tasks. The accuracy of the R2UNet model was 95.6%, while the FF + (AlexResNet + R2Unet) result was more than 97%, with an accuracy (%) of 97.4, AUC (%) of 97.35, precision (%) of 97.4, F1-score (%) of 95.26, and recall (%) of 97.16. The employment of these segmentation approaches in the identification and diagnosis of breast cancer produced outstanding results. Our proposed method could provide a more precise diagnosis of breast cancer, perhaps improving patient outcomes. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 5260 KB

Open AccessProceeding Paper

A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks

by Jagadesh Sambbantham, Gomathy Balasubramanian, Rajarathnam and Mohit Tiwari

Eng. Proc. 2023, 59(1), 45; https://doi.org/10.3390/engproc2023059045 - 13 Dec 2023

Cited by 2 | Viewed by 1589

Abstract

Camouflaged objects are masked within an existing image or video under similar patterns. This makes it tedious to detect target objects post classification. The pattern distributions are monotonous due to similar pixels and non-contrast regions. In this paper, a distribution-differentiated target detection scheme [...] Read more.

Camouflaged objects are masked within an existing image or video under similar patterns. This makes it tedious to detect target objects post classification. The pattern distributions are monotonous due to similar pixels and non-contrast regions. In this paper, a distribution-differentiated target detection scheme (DDTDS) is proposed for segregating and identifying camouflaged objects. First, the image is segmented using textural pixel patterns for which the linear differentiation is performed. Convolutional neural learning is used for training the regions across pixel distribution and pattern formations. The neural network employs two layers for linear training and pattern differentiation. The differentiated region is trained for its positive rate in identifying the region around the target. Non-uniform patterns are used for training the second layer of the neural network. The proposed scheme pursues a recurrent iteration until the maximum segmentation is achieved. The metrics of positive rate, detection time, and false negatives are used for assessing the proposed scheme’s performance. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1614 KB

Open AccessProceeding Paper

The VGG16 Method Is a Powerful Tool for Detecting Brain Tumors Using Deep Learning Techniques

by Sarthak Raghuvanshi and Sumit Dhariwal

Eng. Proc. 2023, 59(1), 46; https://doi.org/10.3390/engproc2023059046 - 14 Dec 2023

Cited by 18 | Viewed by 7240

Abstract

A brain tumor diagnosis is a complex and difficult task that requires accurate and efficient data analysis. In past years, deep learning has emerged as a promising tool for improving the accuracy of mental health diagnoses. This research article presents a review of [...] Read more.

A brain tumor diagnosis is a complex and difficult task that requires accurate and efficient data analysis. In past years, deep learning has emerged as a promising tool for improving the accuracy of mental health diagnoses. This research article presents a review of various in-depth studies and models for mental health diagnosis and examines the performance of convolutional neural networks (CNNs), VGG16, and other deep learning models on multistate data in the brain. The results show that deep learning models can provide high accuracy and efficiency in brain tumor detection beyond imaging techniques to also discuss the clinical applications of these models, including assisting radiologists in brain diagnosis and improving patient outcomes. Overall, this work raises awareness of deep learning’s application in medicine and offers insights into the future of brain tumor research. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1151 KB

Open AccessProceeding Paper

A Futuristic Approach to Security in Cloud Data Centers Using a Hybrid Algorithm

by Dipankar Chatterjee, Mostaque Md. Morshedur Hassan, Nazrul Islam, Asmita Ray and Munsifa Firdaus Khan Barbhuyan

Eng. Proc. 2023, 59(1), 47; https://doi.org/10.3390/engproc2023059047 - 14 Dec 2023

Cited by 2 | Viewed by 2171

Abstract

All associations use on-premises data focus. An on-premises data focus suggests that an association maintains all locally required IT systems. An on-premises data focus consolidates everything from the servers that support Web and email access to the provision of gear and communicates related [...] Read more.

All associations use on-premises data focus. An on-premises data focus suggests that an association maintains all locally required IT systems. An on-premises data focus consolidates everything from the servers that support Web and email access to the provision of gear and communicates related data back to the organization to establish features like uninterruptible control. Data focus organization is not confined to ensuring that an establishments and program strategies are helpful. Data focus chiefs are also responsible for the security of their circumstances. Establishing a data community office is a sensible idea. Most do not have outside windows and, by and large, only a few entrances. Security staff surveil the inside of the structure, screening for dubious activity using footage from observation cameras positioned along the perimeter. This integrates the use of strong security measures, like two-factor confirmation, for all clients. It is also suggested to encrypt all data in movement, both inside the data center and between the data community and any external structures. The components of data centers must be safeguarded against physical threats. A data center’s physical security controls include a secure location, physical access controls for the building, and monitoring systems. As organizations relocate on-premises IT frameworks to cloud specialist co-ops, cloud information capacity, cloud foundations, and cloud applications, it is vital to comprehend the safety strategies they implement and the service-level arrangements they have set up. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 5543 KB

Open AccessProceeding Paper

A Computational Fluid Dynamics Study on Characteristics of Flow Separation in Flow Rate Measurement Using Multi-Hole Plates

by K. J. Mahendra Babu, C. J. Gangadhara Gowda and K. Ranjith

Eng. Proc. 2023, 59(1), 48; https://doi.org/10.3390/engproc2023059048 - 14 Dec 2023

Cited by 2 | Viewed by 4517

Abstract

Flow rate measurement is a challenging task in the industry as there is no general-purpose measuring instrument for all appliances. However, orifice plates with multiple holes can be employed to measure the flow rate accurately. A computational fluid dynamics (CFD)-based numerical study was [...] Read more.

Flow rate measurement is a challenging task in the industry as there is no general-purpose measuring instrument for all appliances. However, orifice plates with multiple holes can be employed to measure the flow rate accurately. A computational fluid dynamics (CFD)-based numerical study was conducted to investigate the flow separation characteristics caused by the flow of water in multiple-hole orifice plates using ANSYS FLUENT R15.0 software. The study included single- and multiple-hole orifice plates, with orifices with a 36% area ratio, an equivalent diameter ratio (β-ratio) of 0.6, and hole number configurations of 1H, 4H, 9H, 16H, and 25H. The discharge coefficient for flow through multiple-hole orifices was obtained and compared for holes distributed in circular and square configurations. The significant parameters considered for the analysis were the hole number, distribution of holes, pressure drop, and reattachment points. A k-ε turbulence model was employed to study velocity fields, reattachment length, and discharge coefficient. We discuss the effects of hole numbers and their allocation on the reattachment length and discharge coefficient. Results are presented in the form of pressure variation comparisons, downstream recovery distance plots, recirculation zone plots, and percentage change in the coefficient of discharge. The study revealed that the number of holes in the plate significantly affects the pressure drop across the plate, the recirculation zone, and the orifice’s discharge coefficient. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1191 KB

Open AccessProceeding Paper

Enhancing Skin Disease Segmentation with Weighted Ensemble Region-Based Convolutional Network

by Nirupama and Virupakshappa

Eng. Proc. 2023, 59(1), 49; https://doi.org/10.3390/engproc2023059049 - 12 Dec 2023

Cited by 7 | Viewed by 1770

Abstract

Skin diseases are a prevalent and diverse group of medical conditions that affect a significant portion of the global population. One critical drawback includes difficulty in accurately diagnosing certain skin conditions, as many diseases can share similar symptoms or appearances. In this paper, [...] Read more.

Skin diseases are a prevalent and diverse group of medical conditions that affect a significant portion of the global population. One critical drawback includes difficulty in accurately diagnosing certain skin conditions, as many diseases can share similar symptoms or appearances. In this paper, we propose a Weighted Ensemble Region-based Convolutional Network (WERCNN) methodology that consolidates a Mask R-CNN (Mask Region-based Convolutional Neural Network) with the weighted average ensemble technique to enhance the performance of segmentation tasks. A skin disease image dataset obtained from kaggle is utilized to segment the skin disease image. This study investigates the utilization of a Mask R-CNN in skin disease segmentation, where it is prepared on a skin disease image dataset of dermatological pictures. The weighted average ensemble model is utilized to optimize the weights of the Mask R-CNN model. The performance metrics accuracy, precision, recall, specificity, and F1-score are to be employed; this can achieve the values of 94.7%, 93.6%, 93.9%, 92.6%, and 93.7%, respectively. With regard to skin disease segmentation, the WERCNN has shown extraordinary in accurately segmenting the impacted regions of skin images by providing valuable insights to dermatologists for diagnosis and treatment planning. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 979 KB

Open AccessProceeding Paper

Multi-Level Cloud Datacenter Security Using Efficient Hybrid Algorithm

by Koushik Chakraborty, Amrita Parashar, Pawan Bhambu, Durga Prasad Tripathi, Pratap Patil and Gaurav Kumar Srivastav

Eng. Proc. 2023, 59(1), 50; https://doi.org/10.3390/engproc2023059050 - 14 Dec 2023

Cited by 1 | Viewed by 1467

Abstract

Security is currently the main boundary for cloud-based administrations. It is not adequate to just consolidate the cloud by adding a couple of additional controls or component answers for your current organization security programming. Businesses must utilize both virtual and physical information center [...] Read more.

Security is currently the main boundary for cloud-based administrations. It is not adequate to just consolidate the cloud by adding a couple of additional controls or component answers for your current organization security programming. Businesses must utilize both virtual and physical information center security frameworks to keep them secure. The objective is to defend it from dangers that may jeopardize the secrecy, judgment, or openness of mental property or commerce data resources. These are the fundamental central focuses of all assigned attacks, and in this way, they require a high degree of security. Hundreds to thousands of physical and virtual servers are partitioned up into information centers agreeing to sort applications, information classification zones, and other criteria. To protect applications, frameworks, information, and clients, information center security takes on the workload over physical information centers and multi-cloud situations. It also applies to open cloud data centers. All server ranches ought to protect their applications and data from a rising number of refined threats and around-the-world ambushes. Each organization is at risk of assault, and numerous organizations have been compromised without being mindful of it. An evaluation of your resources and business necessities is important to improve a spotless way to deal with your way of life and cloud security technique. To deal with a strong mixture of multi-cloud wellbeing program, you should lay out perceivability and control. You can consolidate incredible controls, organize responsibility dispersion, and lay out fantastic gambles on the board with the assistance of safety items and experts. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1973 KB

Open AccessProceeding Paper

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

by Sachinkumar Veerashetty

Eng. Proc. 2023, 59(1), 51; https://doi.org/10.3390/engproc2023059051 - 14 Dec 2023

Viewed by 2323

Abstract

Image processing-based pattern recognition applications often use texture features to identify structural characteristics. Existing algorithms, including statistical, structural, model-based, and transform-based, lack expertise for specialized features extracted around potentially defective regions. This paper proposes an attention-guided deep-learning texture feature extraction algorithm that can [...] Read more.

Image processing-based pattern recognition applications often use texture features to identify structural characteristics. Existing algorithms, including statistical, structural, model-based, and transform-based, lack expertise for specialized features extracted around potentially defective regions. This paper proposes an attention-guided deep-learning texture feature extraction algorithm that can learn features at various regions with varying complexities, addressing the lack of expertise in existing techniques. This approach can be used for applications such as minor fabric defects and hairline faults in PCB manufacturing. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 1811 KB

Open AccessProceeding Paper

Scanning Electron Microcopy Analysis after Electrical Discharge Machining of Advanced Ni-Based Alloy

by Anand Pandey, Ashish Goyal, Ranjan Walia and Varun Jurwall

Eng. Proc. 2023, 59(1), 52; https://doi.org/10.3390/engproc2023059052 - 15 Dec 2023

Viewed by 1339

Abstract

Electrical discharge machining (EDM) and its variant methods are used to fabricate three-dimensional and complex geometrical features from micro level to nano dimensions. Researchers have successfully experimented with high-strength alloys and composite materials, finding wide applications in defense, automobile, and medical industries to [...] Read more.

Electrical discharge machining (EDM) and its variant methods are used to fabricate three-dimensional and complex geometrical features from micro level to nano dimensions. Researchers have successfully experimented with high-strength alloys and composite materials, finding wide applications in defense, automobile, and medical industries to shape precision micro-grooves (straight, tapered, and angular-based). Motion-type EDM methods (when the tool electrode is moving) utilize capabilities to rotate the tool electrode or work material to manufacture grooves (applications included in the micro-electronics sector, aircraft engines, and diffraction gratings). In the present investigation, experimental studies were performed to fabricate the grooves of high-strength NI-based alloy using the EDM electrode (cylindrical in shape) using Taguchi’s L-18 orthogonal array. SEM studies were performed at different magnifications to check and analyze the recast layer formation on the surface of the groove at different parametric settings. The analysis of the effect of input parameters was tested on machine performance responses viz. MRR, EWR, and surface roughness. This was revealed, and the optimum levels of process parameters were analyzed, showing the best surface finish with a maximum metal removal rate after analyzing using SEM. The MRR was found to increase with an increase in the thickness of the disk electrode (0.1–0.6) at all parametric settings. Also, roughness increased with an increase in the current settings from 6 to 12 A. SEM analysis depicts that groove thick ness at the bottom (565 µm) and top of the groove (1.14 mm). Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 5444 KB

Open AccessProceeding Paper

The Effect of Process Parameters on Quality Characteristics in the Drilling of Aluminium–Metal Matrix Composites

by K. B. Vinay, G. V. Naveen Prakash, D. S. Rakshith Gowda, B. S. Nithyananda, K. Ranjith and Srikantamurthy

Eng. Proc. 2023, 59(1), 53; https://doi.org/10.3390/engproc2023059053 - 15 Dec 2023

Cited by 8 | Viewed by 1512

Abstract

Present work focusses on investigating the effect of process parameters such as feed rate and spindle speed on quality characteristics of the hole, i.e., surface roughness (Ra) and circularity at entry and exit in the drilling of aluminium (Al) 6061 reinforced with different [...] Read more.

Present work focusses on investigating the effect of process parameters such as feed rate and spindle speed on quality characteristics of the hole, i.e., surface roughness (Ra) and circularity at entry and exit in the drilling of aluminium (Al) 6061 reinforced with different volume fraction of silicon nitride (Si₃N₄). Optimum parameters for Ra and circularity of hole at entry and exit are obtained as feed rate at 0.125 mm/rev, spindle speed at 300 rpm, diameter of drill at 8 mm, and % Vol. of Si₃N₄ at 5%. Using Analysis of Variance (ANOVA), we observed that spindle speed is the most influential parameter followed by feed rate. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 223 KB

Open AccessProceeding Paper

Combining Forth and Rust: A Robust and Efficient Approach for Low-Level System Programming

by Priya Gupta, Ravi Rahar, Rahul Kumar Yadav, Ajit Singh, Ramandeep and Sunil Kumar

Eng. Proc. 2023, 59(1), 54; https://doi.org/10.3390/engproc2023059054 - 17 Dec 2023

Cited by 1 | Viewed by 3229

Abstract

Rust is a modern programming language that addresses the drawbacks of earlier languages by providing features such as memory safety at compilation and high performance. Rust’s memory safety features include ownership and borrowing, which makes it an ideal choice for systems programming, where [...] Read more.

Rust is a modern programming language that addresses the drawbacks of earlier languages by providing features such as memory safety at compilation and high performance. Rust’s memory safety features include ownership and borrowing, which makes it an ideal choice for systems programming, where memory safety is critical. Forth is a stack-based programming language that is widely used for low-level system programming due to its simplicity and ease of use. This research paper aims to explore the combination of Forth and Rust programming languages to create a more robust and efficient solution for low-level system programming. The primary objective is to demonstrate the implementation of essential Forth operations, including addition, subtraction, assignment, comparison, and if-else statements, while demonstrating loops, push operations, and dump operations in Rust. The implementation of these operations in Rust is demonstrated using code from actual implementation. This research paper also discusses the advantages of using Rust for low-level system programming. Rust’s memory safety features, coupled with its high performance, make it an ideal choice for systems programming, where memory safety and performance are critical. The combination of Forth and Rust provides a more efficient and safer solution for low-level system programming, making the implementation more robust. Our implementation tries to leverage these properties of both languages to make a memory-safe and low-level system programming language. This research paper also includes code snippets to provide a practical demonstration of how the Forth operations can be implemented in Rust. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

8 pages, 1125 KB

Open AccessProceeding Paper

Mathematical Models to Compare the Pharmacokinetics of Methadone, Buprenorphine, Tramadol, and Tapentadol

by Prathvi Shenoy, Joslin D’Souza, Mahadev Rao, Shreesha Chokkadi and Naveen Salins

Eng. Proc. 2023, 59(1), 55; https://doi.org/10.3390/engproc2023059055 - 18 Dec 2023

Viewed by 3851

Abstract

The study of a drug’s absorption, distribution, metabolization, and excretion by the body is known as pharmacokinetics (PK). In pharmacokinetics, the two-compartment model is used to understand the distribution and elimination of drugs. The two-compartment model represents the body as two distinct compartments: [...] Read more.

The study of a drug’s absorption, distribution, metabolization, and excretion by the body is known as pharmacokinetics (PK). In pharmacokinetics, the two-compartment model is used to understand the distribution and elimination of drugs. The two-compartment model represents the body as two distinct compartments: the central compartment (such as the blood) and the peripheral compartment (such as tissues). This work aims to enhance the understanding of drug kinetics inside the human body by comparing different mathematical models. The important focus of this study is to compare the distribution patterns of the drugs methadone, buprenorphine, tramadol, and tapentadol when administered intravenously using a two-compartment model. To mathematically describe the distribution of drugs in the body, a system of nonlinear ordinary differential equations is employed. These equations capture the dynamics of drug concentration in the different compartments over time. The roots are obtained by solving this system of equations using numeric analysis techniques. The study determines the duration of the drugs to attain the minimum effective concentration in the blood by analyzing the obtained results. Furthermore, the study also determines the time it takes for these drugs to be eliminated from the body. This data is significant for understanding the drug’s clearance rate and its potential duration of action. By comparing the distribution patterns and elimination rates of methadone, buprenorphine, tramadol, and tapentadol, the study provides insights into the differences between these drugs in terms of their pharmacokinetic properties. Healthcare professionals can utilize this information to optimize drug therapy, ensuring that the drugs are administered in accurate amounts and at precise intervals to target the desired therapeutic effect. Overall, this study provides a comprehensive analysis of drug kinetics, aiding in a better understanding of drug behavior within the human body and facilitating informed decision making in clinical settings. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1590 KB

Open AccessProceeding Paper

Dynamic Analysis of 650 W Vertical-Axis Wind Turbine Rotor System Supported by Radial Permanent Magnet Bearings

by Gireesha R. Chalageri, Siddappa I. Bekinal and Mrityunjay Doddamani

Eng. Proc. 2023, 59(1), 56; https://doi.org/10.3390/engproc2023059056 - 18 Dec 2023

Cited by 3 | Viewed by 2453

Abstract

This paper presents a comprehensive dynamic analysis of a 650 W vertical-axis wind turbine (VAWT) rotor system, focusing on the impact of radial permanent magnet bearings (PMBs) on its performance. Through optimization of PMB capacity and stiffness using multi-ring radially magnetized stack structures, [...] Read more.

This paper presents a comprehensive dynamic analysis of a 650 W vertical-axis wind turbine (VAWT) rotor system, focusing on the impact of radial permanent magnet bearings (PMBs) on its performance. Through optimization of PMB capacity and stiffness using multi-ring radially magnetized stack structures, the study explores their influence on modal frequency, vibration amplitude, and system stability. The research progresses through steps, initially analyzing the rotor system with deep groove ball bearings (DGBs), considering the bearing span length, and transitioning to a hybrid bearing set (HBS) with PMBs. Ultimately, the rotor system entirely relies on radial PMBs, as investigated through finite element analysis (FEA). The results reveal significant improvements in critical speeds (5.75–9.81 percent higher than operational speeds), emphasizing the influence of bearing stiffness on system dynamics and stability. The study’s insights offer valuable contributions to the understanding and design optimization of VAWT rotor systems supported by PMBs, enhancing the efficiency and reliability of wind energy conversion systems. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 979 KB

Open AccessProceeding Paper

Container Security in Cloud Environments: A Comprehensive Analysis and Future Directions for DevSecOps

by Santosh Ugale and Amol Potgantwar

Eng. Proc. 2023, 59(1), 57; https://doi.org/10.3390/engproc2023059057 - 18 Dec 2023

Cited by 10 | Viewed by 6503

Abstract

In recent years, the security of containers has become a crucial aspect of modern software applications’ security and integrity. Containers are extensively used due to their lightweight and portable nature, allowing swift and agile deployment across different environments. However, the increasing popularity of [...] Read more.

In recent years, the security of containers has become a crucial aspect of modern software applications’ security and integrity. Containers are extensively used due to their lightweight and portable nature, allowing swift and agile deployment across different environments. However, the increasing popularity of containers has led to unique security risks, including vulnerabilities in container images, misconfigured containers, and insecure runtime environments. Containers are often built using public repository images and base image vulnerability is inherited by containers. Container images may contain outdated components or services, including system libraries and dependencies and known vulnerabilities from these components can be exploited. Images downloaded from untrusted sources may include malicious code that compromises other containers running in the same network or the host system. Base images may include unnecessary software or services that increase the attack surface and potential vulnerabilities. Several security measures have been implemented to address these risks, such as container image scanning, container orchestration security, and runtime security monitoring. Implementing a solid security policy and updating containers with the latest patches can significantly improve container security. Given the increasing adoption of containers, organizations must prioritize container security to protect their applications and data. This work presents automated, robust security techniques for continuous integration and continuous development pipelines, and the added overhead is empirically analyzed. Then, we nail down specific research and technological problems the DevSecOps community encounters and appropriate initial fixes. Our results will make it possible to make judgments that are enforced when using DevSecOps techniques in enterprise security and cloud-native applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 748 KB

Open AccessProceeding Paper

A Natural Language Processing Model for Predicting Five-Star Ratings of Video Games on Short-Text Reviews

by Piyush Jaiswal, Hardik Setia, Pranshu Raghuwanshi and Princy Randhawa

Eng. Proc. 2023, 59(1), 58; https://doi.org/10.3390/engproc2023059058 - 18 Dec 2023

Cited by 5 | Viewed by 3338

Abstract

The gaming industry is one of the most important and innovative subfields in the field of technology, which boasts a staggering USD 200 billion in annual revenue and stands as a behemoth. It has an immense effect on popular culture, social networking, and [...] Read more.

The gaming industry is one of the most important and innovative subfields in the field of technology, which boasts a staggering USD 200 billion in annual revenue and stands as a behemoth. It has an immense effect on popular culture, social networking, and the entertainment industry. Continuous advances in technology are the primary factor fueling the industry’s expansion, and these innovations are also revolutionizing the design of games and improving the overall gaming experience for players. The growing number of people who have access to the internet, the widespread use of smartphones, and the introduction of high-bandwidth networks such as 5G have all contributed to an increase in the demand for gaming around the world. It is essential to perform consumer feedback analysis if one wants to appreciate market requirements, evaluate game performance, and realize the effect that games have on players. On the other hand, short-text reviews frequently lack grammatical syntax, which makes it difficult for standard natural language processing (NLP) models to effectively capture underlying values and, as a result, compromises the accuracy of these models. This research focuses on determining which natural language processing model is the most accurate at forecasting five-star ratings of video games based on brief reviews. We make use of natural language processing (NLP) to avoid the constraints that are imposed on us by the linguistic structure of short-text reviews. The findings of our research have led to several important contributions, one of which is the creation of an innovative model for reviewing and grading short writings. The accuracy is improved by employing different machine learning models, which enables game creators and other industry stakeholders to identify patterns about the behavior and preferences of the users. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 460 KB

Open AccessProceeding Paper

Key Generation in Cryptography Using Elliptic-Curve Cryptography and Genetic Algorithm

by Sanjay Kumar and Deepmala Sharma

Eng. Proc. 2023, 59(1), 59; https://doi.org/10.3390/engproc2023059059 - 18 Dec 2023

Cited by 15 | Viewed by 5250

Abstract

Elliptic-curve cryptography (ECC) has become a robust cryptographic technique that ensures secure data transmission with comparatively small key sizes. In this context, this research introduces a novel approach to ECC-key-pair generation by utilizing genetic algorithms (GAs). GAs have proven effective in solving optimization [...] Read more.

Elliptic-curve cryptography (ECC) has become a robust cryptographic technique that ensures secure data transmission with comparatively small key sizes. In this context, this research introduces a novel approach to ECC-key-pair generation by utilizing genetic algorithms (GAs). GAs have proven effective in solving optimization problems by mimicking the principles of natural selection and genetics. The proposed genetic algorithm-based ECC-key generation process involves several stages: chromosome initialization, fitness evaluation, selection, uniform crossover, and mutation. Chromosomes representing points on an elliptic curve are initialized randomly, evaluated for their proximity to a predefined target point using a fitness function, and subjected to tournament selection to determine parents for the next generation. Uniform crossover and mutation operators then create offspring, inheriting traits from their parents while introducing diversity. The generated ECC-key pair comprises private and public keys derived from the GA-driven process. The private key is chosen randomly within the constraints of the elliptic curve’s parameters, while the public key is generated through the GA procedure. The study evaluates the efficiency and effectiveness of the proposed ECC-GA approach through an empirical analysis of execution time, key size, and the size of the search space. The outcomes of this research highlight the potential of genetic algorithms in ECC-key generation, offering a promising alternative for enhancing the security and efficiency of cryptographic systems, especially in resource-limited environments. The exploration of key size and search space may assist in understanding the security implications and computational complexity associated with the proposed method. Overall, the ECC-GA approach opens avenues for further research in innovative key-generation techniques for modern cryptographic applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 522 KB

Open AccessProceeding Paper

The Waning Intellect Theory: A Theory on Ensuring Artificial Intelligence Security for the Future

by Pankaj Sarsia, Akhileshwer Munshi, Aradhya Joshi, Vanshita Pawar and Aashrya Shrivastava

Eng. Proc. 2023, 59(1), 60; https://doi.org/10.3390/engproc2023059060 - 18 Dec 2023

Viewed by 2046

Abstract

In the era of rapid technological advancement, understanding and confronting the challenges posed by AI systems are imperative. The concept of Superintelligence denotes the potential for AI to surpass the intellectual capacities of even the most brilliant human minds. As AI capabilities outpace [...] Read more.

In the era of rapid technological advancement, understanding and confronting the challenges posed by AI systems are imperative. The concept of Superintelligence denotes the potential for AI to surpass the intellectual capacities of even the most brilliant human minds. As AI capabilities outpace human intellect and continually evolve, achieving such Superintelligence could lead to a point of no return—technological singularity—with uncontrollable repercussions, risking humanity’s existence. The proposed Waning Intellect theory suggests placing a finite lifespan on AI models to prevent unchecked evolution. Waning Intellect anticipates potential diminishing AI capabilities due to increased neural network complexity, posing risks to reliability, safety, and ethical concerns. Upholding ethical standards, human–AI collaboration, and robust regulatory frameworks are pivotal in leveraging AI’s potential while ensuring responsible deployment and mitigating risks. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 937 KB

Open AccessProceeding Paper

Efficient Execution of Cloud Resource Management in Cloud and Internet of Things Applications

by Preeti Narooka, Nancy Arya, Nazeer Shaik, Surendra Kumar, Durga Prasad Tripathi and Arvind Kumar Singh

Eng. Proc. 2023, 59(1), 61; https://doi.org/10.3390/engproc2023059061 - 18 Dec 2023

Cited by 2 | Viewed by 2420

Abstract

The Internet of Things is essential for business. It makes it possible to gather and analyze huge amounts of data in real time. IoT devices also encourage computerization. They enable individuals to gain greater control over their circumstances, well-being, and safety. As a [...] Read more.

The Internet of Things is essential for business. It makes it possible to gather and analyze huge amounts of data in real time. IoT devices also encourage computerization. They enable individuals to gain greater control over their circumstances, well-being, and safety. As a rule, there are two principal sorts of asset the executives move toward that concern framework and applications. All improvement groups that work with cloud situations will be influenced by the modern approaches to cloud administration. Utilization checking, asset assignment to applications and administrations based on their prerequisites, and capacity administration— all components of asset administration—guarantee that assets are utilized successfully. It might, for instance, utilize robotized apparatuses to screen how its servers are being utilized, donate more assets to administrations that are in great demand, and cut back on administrations that are not in great demand. The Internet of Things makes it conceivable to computerize regular undertakings that commonly consume a ton of assets and labor supply; thus, trading settings considering brief environment or use is one model. This opens a great deal of assets, permitting the organization to focus on development and a bigger vision of the business. It provides information to encourage better choices and tracks down holes in tasks, cycles, and business arrangements. It likewise makes an extraordinary association between the production line floor and the business. This implies expanded efficiency, even while reducing expenses and energy use. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 2848 KB

Open AccessProceeding Paper

Heat Transfer Enhancement in a Tube Heat Exchanger Using Discrete Triangular-Prism Roughness Elements

by Dolfred Vijay Fernandes

Eng. Proc. 2023, 59(1), 62; https://doi.org/10.3390/engproc2023059062 - 18 Dec 2023

Cited by 1 | Viewed by 1680

Abstract

Heat exchangers of high effectiveness are generally sought by the thermal industry for the efficient utilization of heat energy. The present study focuses on the enhancement of the effectiveness of a single-tube heat exchanger by attaching equilateral triangular-prism roughness elements on the peripheral [...] Read more.

Heat exchangers of high effectiveness are generally sought by the thermal industry for the efficient utilization of heat energy. The present study focuses on the enhancement of the effectiveness of a single-tube heat exchanger by attaching equilateral triangular-prism roughness elements on the peripheral heat transfer surface. The forced convection in a circular tube is analyzed using ANSYS-fluent considering air as the working fluid in the Reynolds number (Re) range of 10,000 to 18,000. The geometric parameters (the cross-section and the roughness element height) are fixed. The effects of longitudinal pitch, angular pitch and the orientation of triangular-prism roughness elements on the heat transfer and the frictional energy loss are studied. The presence of roughness elements on the heat transfer surface is found to increase turbulence and fluid mixing. Up to a 23% increase in heat transfer performance is seen in the Nusselt number values of the roughened tube over the smooth tube. The presence of roughness elements on the tube surface also increases the frictional losses; however, this increase is found to be gradual with the reduction in both longitudinal and angular pitch. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1709 KB

Open AccessProceeding Paper

The Machine Learning-Based Task Automation Framework for Human Resource Management in MNC Companies

by Suchitra Deviprasad, N. Madhumithaa, I. Walter Vikas, Archana Yadav and Geetha Manoharan

Eng. Proc. 2023, 59(1), 63; https://doi.org/10.3390/engproc2023059063 - 18 Dec 2023

Cited by 48 | Viewed by 5043

Abstract

Recently, machine learning-based task automation framework have been gaining attention in human resource management of Multi-National Companies (MNCs). Task automation framework helps MNCs to automate repetitive HR tasks, analyse data quickly and accurately, forecast workforce, and recognize employees. MNCs are now beginning to [...] Read more.

Recently, machine learning-based task automation framework have been gaining attention in human resource management of Multi-National Companies (MNCs). Task automation framework helps MNCs to automate repetitive HR tasks, analyse data quickly and accurately, forecast workforce, and recognize employees. MNCs are now beginning to use ML algorithms in combination with Artificial Intelligence (AI) to streamline the HR processes. Most MNCs have large-scale operations and decentralized organization structures which put additional pressure on HR teams to carry out intricate and tedious manual processes. To ease the process, ML-based task automation framework facilitates HR teams to leverage the power of AI and perform HR management tasks in a more effective and efficient manner. The ML-based task automation framework utilizes automation bots which can simulate all processes of HR management such as recruitment, time attendance, tracking employee records, scheduling calendar, and office administration tasks. The machine learning-based task automation framework utilizes predictive analytics to identify trends, patterns, behaviour, anomalies, and important insights from the large volumes of structured and unstructured data. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 266 KB

Open AccessProceeding Paper

Sustainability in Supply Chain Management: A Case Study of the Indian Retailing Industry

by Rajasekhara Mouly Potluri and Madhavi Kilaru

Eng. Proc. 2023, 59(1), 64; https://doi.org/10.3390/engproc2023059064 - 18 Dec 2023

Cited by 3 | Viewed by 4199

Abstract

This study aims to identify the sustainability programs introduced in their supply chains by the Indian retailing (FMCG and Pharma) sector and the various problems encountered in managing their supply chains. The researchers collected the opinions of 200 companies from the FMCG and [...] Read more.

This study aims to identify the sustainability programs introduced in their supply chains by the Indian retailing (FMCG and Pharma) sector and the various problems encountered in managing their supply chains. The researchers collected the opinions of 200 companies from the FMCG and pharma sectors after checking the questionnaire’s internal consistency and validity using Cronbach’s α and Kaiser–Meyer–Olkin (KMO) tests. After data collection, the data were summarized, coded, and controlled using R Studio and Microsoft Excel. The hypotheses were analyzed using the Kruskal–Wallis (K-W) hypothesis technique. Manufacturers emphasized that their supply chains impact toxic waste and pollution, that wholesalers and retailers are highly influenced by poor cost control and management, that there is a difficulty in forecasting demand, and that there are supply related problems. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

9 pages, 1645 KB

Open AccessProceeding Paper

An Enhanced Analysis of Blood Cancer Prediction Using ANN Sensor-Based Model

by Althaf Ali A, K. Hemalatha, N. Mohana Priya, S. Aswath and Sushma Jaiswal

Eng. Proc. 2023, 59(1), 65; https://doi.org/10.3390/engproc2023059065 - 18 Dec 2023

Cited by 5 | Viewed by 3367

Abstract

Blood cancer diagnosis is a critical medical procedure, yet difficult and expensive for clinical personnel to perform accurately. Artificial neural networks have been shown to be effective in diagnosing a range of diseases, due to their powerful ability to identify and classify patterns [...] Read more.

Blood cancer diagnosis is a critical medical procedure, yet difficult and expensive for clinical personnel to perform accurately. Artificial neural networks have been shown to be effective in diagnosing a range of diseases, due to their powerful ability to identify and classify patterns in data. Here, we present a study that employed one such ANN to diagnose blood cancer from data gathered from network sensors. First, a sensor network was placed in an animal model to capture various physiological data, including cardiac and respiratory rates, body temperature, and blood pressure. This data was then sent to an ANN which used a classification system based on the type of cancer for diagnostic analysis. Our results showed that the ANN was able to accurately diagnose a blood cancer with an accuracy of 92.1% and that its accuracy improved with the addition of more data. Our study demonstrates that ANNs can be successfully used to accurately diagnose blood cancer using data from network sensors, which could reduce costs and provide faster results in clinical settings. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 6536 KB

Open AccessProceeding Paper

Comparative Analysis of Crystalline Silicon Solar Cell Characteristics in an Individual, Series, and Parallel Configuration and an Assessment of the Effect of Temperature on Efficiency

by Vanshika Bhalotia and Prathvi Shenoy

Eng. Proc. 2023, 59(1), 66; https://doi.org/10.3390/engproc2023059066 - 18 Dec 2023

Viewed by 2272

Abstract

Solar energy is gaining immense significance as a renewable energy source owing to its environmentally friendly nature and sustainable attributes. Crystalline silicon solar cells are the prevailing choice for harnessing solar power. However, the efficiency of these cells is greatly influenced by their [...] Read more.

Solar energy is gaining immense significance as a renewable energy source owing to its environmentally friendly nature and sustainable attributes. Crystalline silicon solar cells are the prevailing choice for harnessing solar power. However, the efficiency of these cells is greatly influenced by their configuration and temperature. This research aims to explore the current–voltage (I−V) characteristics of individual, series, and parallel configurations in crystalline silicon solar cells under varying temperatures. Additionally, the impact of different temperature conditions on the overall efficiency and Fill Factor of the solar cell was analyzed. With the aid of a solar simulator and required conditions, the I−V characteristics of each configuration—individual, series, and parallel—were obtained. The solar panel was subjected to various temperature settings, and I−V characteristics were obtained for each configuration to calculate the maximum power and Fill Factor for each case. In addition to this, the results showed that the parallel configuration has a larger power output, followed by the individual and series configurations. Additionally, the temperature of the solar panel had a significant effect on the output power of the solar cells. The maximum output power is also affected by temperature variation. The Fill Factor, on the other hand, was observed to be dependent on the configuration but had no significant variation with respect to the temperature. The effect of solar irradiance was also observed in a configuration with a definite temperature. This research offers valuable insights into the ideal configuration and optimal temperature for achieving maximum efficiency in crystalline silicon solar cells. Hence, a definite configuration with optimum temperature yields maximum power output and helps in attaining maximum efficiency. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 9743 KB

Open AccessProceeding Paper

Helical Milling and Drilling for Hole-Making in CARALL: Experimental Evaluation

by Madhusudhan Balkundhi, Satish Shenoy Baloor and Gururaj Bolar

Eng. Proc. 2023, 59(1), 67; https://doi.org/10.3390/engproc2023059067 - 19 Dec 2023

Cited by 3 | Viewed by 1689

Abstract

Carbon fiber-reinforced aluminum laminates, known as CARALL, have wide applications in aircraft structures. However, numerous holes must be processed to assemble these structures, which is conventionally practiced through drilling. However, the drilling process exhibits certain limitations when utilized for hole-making in heterogeneous materials. [...] Read more.

Carbon fiber-reinforced aluminum laminates, known as CARALL, have wide applications in aircraft structures. However, numerous holes must be processed to assemble these structures, which is conventionally practiced through drilling. However, the drilling process exhibits certain limitations when utilized for hole-making in heterogeneous materials. In the recent past, helical milling has positioned itself as an alternative to the drilling process. However, helical milling performance examination during hole-making in CARALL is scant and needs further evaluation. The present study compares the milling process to the drilling process considering important performance indices, including cutting forces, surface roughness, chip morphology, machining temperature, and burr size. Additionally, microscopic characterization of the boreholes is performed to verify the presence of surface damage and delamination defects. Helical milling successfully lowered the thrust and radial forces and restrained the machining temperature below the levels attained via drilling. The diametrical deviation is higher at entry and lower at exit for both processes; however, helical milling produced holes with much higher accuracy. Helical milling developed smaller sized holes in comparison to drilling. Moreover, rougher surfaces due to the abrasion of continuous chips were observed in drilling, while a smoother finish was noted in helically milled holes. Based on the comprehensive comparative analysis, helical milling positions itself as an acceptable alternative to conventional drilling for machining fiber metal laminates. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 770 KB

Open AccessProceeding Paper

An Analysis of Sentiment: Methods, Applications, and Challenges

by Harish Dutt Sharma and Parul Goyal

Eng. Proc. 2023, 59(1), 68; https://doi.org/10.3390/engproc2023059068 - 19 Dec 2023

Cited by 21 | Viewed by 16295

Abstract

Sentiment analysis involves contextually examining text to identify and extract subjective information from source material. It aids businesses in comprehending the public sentiment surrounding their brand, product, or service while monitoring online discussions. Nevertheless, analyzing social media content is often limited to basic [...] Read more.

Sentiment analysis involves contextually examining text to identify and extract subjective information from source material. It aids businesses in comprehending the public sentiment surrounding their brand, product, or service while monitoring online discussions. Nevertheless, analyzing social media content is often limited to basic sentiment analysis and simple count-based metrics. Devices that allow the collection of huge amounts of unstructured, opinionated data are becoming increasingly connected with humans. Everyday-activity-related comments and evaluations have been obtained as a result of the advances in Internet-based services like social media platforms and blogs. This study supplies a comprehensive assessment of sentiment analysis approaches to provide academics with a global perspective on the analysis of feelings and its associated domain, applications, and challenges. To comprehend the applications of sentiment analysis, this article provides a detailed explanation of the technique for performing this activity. To comprehend the benefits and drawbacks of each method, they are then evaluated, compared, and discussed. To establish future perspectives, the difficulties of sentiment analysis are finally evaluated. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2958 KB

Open AccessProceeding Paper

Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh

by Sachi Nandan Mohanty, Hritwik Ghosh, Irfan Sadiq Rahat and Chirra Venkata Rami Reddy

Eng. Proc. 2023, 59(1), 69; https://doi.org/10.3390/engproc2023059069 - 19 Dec 2023

Cited by 41 | Viewed by 4520

Abstract

Agriculture is pivotal in Bangladesh, with maize being a central crop. However, leaf diseases significantly threaten its productivity. This study introduces deep learning models for enhanced disease detection in maize. We developed an unique datasets of 4800 maize leaf images, categorized into four [...] Read more.

Agriculture is pivotal in Bangladesh, with maize being a central crop. However, leaf diseases significantly threaten its productivity. This study introduces deep learning models for enhanced disease detection in maize. We developed an unique datasets of 4800 maize leaf images, categorized into four health conditions: Healthy, Common Rust, Gray Leaf Spot, and Blight. These images underwent extensive Pre-processing and data augmentation to improve robustness. We explored various deep learning models, including ResNet50GAP, DenseNet121, VGG19, and a custom Sequential model. DenseNet121 and VGG19 showed exceptional performance, achieving accuracies of 99.22% and 99.44% respectively. Our research is novel due to the integration of transfer learning and image augmentation, enhancing the models’ generalization capabilities. A hybrid model combining ResNet50 and VGG16 features achieved a remarkable 99.65% accuracy, validating our approach. Our results indicate that deep learning can significantly impact agricultural diagnostics, offering new research directions and applications. This study highlights the potential artificial intelligence in advancing agricultural practices and food security in Bangladesh, emphasizing the need for model interpretability to build trust in machine learning solutions. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 1254 KB

Open AccessProceeding Paper

Novel Class of Benzimidazoles: Synthesis, Characterization and Pharmaceutical Evaluation

by Siddesh M. Basavaraja, Manjunatha C. Ramegowda, Umesha K. Bhadraiah, Vrushabendra Basavanna, Chandramouli Manasa, Dileep C. Shanthakumar and Srikantamurthy Ningaiah

Eng. Proc. 2023, 59(1), 70; https://doi.org/10.3390/engproc2023059070 - 19 Dec 2023

Cited by 2 | Viewed by 3178

Abstract

The wide range of biological processes and functions that benzimidazole moieties can be used for makes them very interesting synthetic molecules. A novel class of scaffolds for benzimidazole heterocycles has been successfully constructed in the present study and synthesized by using the starting [...] Read more.

The wide range of biological processes and functions that benzimidazole moieties can be used for makes them very interesting synthetic molecules. A novel class of scaffolds for benzimidazole heterocycles has been successfully constructed in the present study and synthesized by using the starting material of O-phenylenediamine derivatives (1a–c). The 1-methyl-2-(methylthio)-1H-benzo[d]imidazole derivatives (3a–c) have been synthesized as intermediate compounds by treating the precursors (1a–c) with carbon disulfide followed by N- and S-methylation with iodomethane and anhydrous potassium carbonate. In the latter step, the intermediate molecules were converted into benzimidazole-containing methyl-piperazine (4a–c), piperazin-ol tethered benzimidazoles (5a–c), and phenylpiperazine holding benzimidazoles (6a–c). The structures assigned to target compounds have been analyzed and confirmed via IR, NMR, and MS analysis. The antibacterial, anthelmintic, and anticancer properties of the target compounds were examined. The biological study showed that the compounds 6b, 4c, and 5a emerge as excellent antibacterial, antifungal, and anthelmintic agents, respectively, whereas heterocycle 6a showed excellent anticancer activity against hepatocyte-derived cell line HUH7, as well as the MCF7 breast cancer cell line with IC₅₀ values of 6.41 and 9.70 µg/mL, respectively. The discovery of a novel class of hetero compounds with multiple hetero moieties that may aid in medication design is also highlighted in this study. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1194 KB

Open AccessProceeding Paper

Fault Detection and Classification in Electrical Power Transmission System Using Wavelet Transform

by Bharathwaaj Sundararaman and Prateek Jain

Eng. Proc. 2023, 59(1), 71; https://doi.org/10.3390/engproc2023059071 - 19 Dec 2023

Cited by 13 | Viewed by 4399

Abstract

A balanced operating power system with all elements carrying normal currents and bus voltages within the prescribed limits can be disrupted due to faults within the system. Overhead transmission networks are vulnerable to the vagaries of the atmosphere and, therefore, statistically have the [...] Read more.

A balanced operating power system with all elements carrying normal currents and bus voltages within the prescribed limits can be disrupted due to faults within the system. Overhead transmission networks are vulnerable to the vagaries of the atmosphere and, therefore, statistically have the highest probability of fault occurrence. Quick and accurate fault detections assist in timely remedial action, offering significant economic and operational benefits. Maintaining continuous and uninterrupted supply functionality is one of the critical objectives of electric utilities for a reliable system operation. Also, identifying and locating faults is crucial to address them in time to avert the risk of cascading failures. During faults, fast electromagnetic transients associated with the current and voltage waveforms can provide valuable insights into identifying abnormal operating conditions. To analyze these non-stationary signals in both the time and frequency domains, wavelet transform (WT) has become an indispensable tool. Thanks to its ability to adapt to variable window sizes, WT provides a more accurate and detailed resolution, making it a highly useful technique for signal analysis. In this context, this paper presents the application of WT-based intelligent technique to detect and classify power system faults accurately. The transient disturbances caused by various faults are subjected to wavelet transform analysis to analyze the detail coefficients of phase currents. The maximum detail coefficients of phase currents, which differ significantly when the system experiences a fault, served as the distinguishing feature to identify different power system faults. The phase current signals are analyzed with one of the wavelets from the Daubechies 4 (db4) family to obtain detail coefficients, thus enabling the categorization of the faults. Extensive simulation tests for fault types have been conducted on the standard IEEE 5-Bus system to demonstrate the technique’s effectiveness and fault detection capability, allowing utilities to take timely protective actions. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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13 pages, 21056 KB

Open AccessProceeding Paper

Characterization of Aluminium Alloy LM6 with B₄C and Graphite Reinforced Hybrid Metal Matrix Composites

by Suresh B. Somegowda, Manjunath S. Honnaiah and Girish K. Bettaiah

Eng. Proc. 2023, 59(1), 72; https://doi.org/10.3390/engproc2023059072 - 19 Dec 2023

Cited by 5 | Viewed by 2930

Abstract

Hybrid metal matrix composites (MMCs) are increasingly important in aviation, marine, automotive, and industrial manufacturing due to their ability to enhance the mechanical and chemical properties of composites. The study aimed to understand the fabrication, mechanical properties and microstructural properties of LM6/B4C/Gr composites. [...] Read more.

Hybrid metal matrix composites (MMCs) are increasingly important in aviation, marine, automotive, and industrial manufacturing due to their ability to enhance the mechanical and chemical properties of composites. The study aimed to understand the fabrication, mechanical properties and microstructural properties of LM6/B4C/Gr composites. An aluminium alloy (LM6) is the base metal, having properties of less weight, medium strength, and excellent castability. The addition of B4C and Gr enhanced the tensile strength, hardness, and wear resistance of the composites, while maintaining good ductility. Boron carbide is a lightweight and extremely hard material with excellent wear resistance and high thermal stability. It has a specific modulus that is almost two times higher than that of aluminium, meaning it can provide superior stiffness and strength while maintaining a low weight such as drive shafts, housings, and structural supports. The addition of graphite improves the lubrication properties of the composites. Composites were successfully fabricated through a stir casting process, with the uniform dispersion of boron carbide and graphite particles in the aluminium LM6 matrix. The hybrid metal matrix composites are fabricated by five different combinations of B4C (1, 2, 3, 4, 5 wt%) with constant wt% of graphite (1 wt%).The fabricated samples of hybrid composites used to find the mechanical properties and microstructure analysis. The test results reveal that the tensile strength and hardness of the composites increased with an increase in the weight percentage of reinforcements, and the percentage of elongation decreases with increasing the reinforcement particles. The boron carbide (B₄C) and graphite (Gr)particles in a matrix material are analyzed by a scanning electron microscope (SEM). Energy dispersive X-ray analysis (EDX) is used to evaluate the microstructure and chemical composition of the composites, providing valuable insights for their design and optimization. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1383 KB

Open AccessProceeding Paper

Intelligent Machine Learning Based Internet of Things (IoT) Resource Allocation

by Koushik Chakraborty, Dhiraj Kapila, Sumit Kumar, Bhupati, Nazeer Shaik and Akanksha Singh

Eng. Proc. 2023, 59(1), 73; https://doi.org/10.3390/engproc2023059073 - 19 Dec 2023

Cited by 6 | Viewed by 2590

Abstract

The Internet of Things (IoT) and machine learning provide insights that would otherwise be hidden in data for quicker, automated responses and improved decision-making. By ingesting images, videos, and audio, machine learning for the Internet of Things can be used to predict future [...] Read more.

The Internet of Things (IoT) and machine learning provide insights that would otherwise be hidden in data for quicker, automated responses and improved decision-making. By ingesting images, videos, and audio, machine learning for the Internet of Things can be used to predict future trends, identify anomalies, and enhance intelligence. The IoT organic framework comprises millions of sharp objects, and to form these sharp objects to communicate and work suitably, asset tasks are necessary. Protection of the quality of service (QoS) is one of the diverse reasons that resource tasks ought to be performed. These techniques aid accomplices in choosing tasks resulting in preeminent regard and impact. Prebuilt software-as-a-service (SaaS) applications, called IoT Cleverly applications, can use dashboards to analyze and display data from IoT sensors. If one of the devices is hacked, the security of every other device in this chain is compromised. This can possibly result in second thoughts about a security plans. A user can see key execution indicators and measure the time between data entries by using IoT dashboards and alarms. Calculations based on machine learning can find peculiarities in equipment, notify customers, and even start robotized repairs or proactive countermeasures. AI and Profound Learning resemble managing a real workplace issue such as marking by combining a few innovations that enable constant naming. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1102 KB

Open AccessProceeding Paper

Comparison of Footrest Vibrations in the Case of an ICE-Based and Battery-Based Two-Wheeler

by Keerthan Krishna, Sriharsha Hegde, Gonuru Thammanaiah Mahesha and Satish Shenoy Baloor

Eng. Proc. 2023, 59(1), 74; https://doi.org/10.3390/engproc2023059074 - 19 Dec 2023

Cited by 2 | Viewed by 1711

Abstract

The current work investigates the comfort of two-wheeler riders and compares the footrest vibration between an internal combustion-engine-based and electric two-wheeler. The Retrofit Hero Honda CD-100 two-wheeler is considered for the study and is further converted into the electric mode in the laboratory. [...] Read more.

The current work investigates the comfort of two-wheeler riders and compares the footrest vibration between an internal combustion-engine-based and electric two-wheeler. The Retrofit Hero Honda CD-100 two-wheeler is considered for the study and is further converted into the electric mode in the laboratory. Electric two-wheelers, even though they have fewer moving parts than internal combustion engine-based two-wheelers, encounter vibrations that emerge from road excitations. Cracks, potholes, and irregular humps on the road are the major influencers of these vibrations. These vibrations, when they transfer to the human body, have been reported to cause major injuries to the human body in the long run. By performing several trials on actual road conditions, with both the rider as well as pillion, the vibration dose value is calculated at the footrest. Different scenarios, such as a random speed test, a 20 kmph speed test and a 30 kmph speed test, are conducted on the two-wheeler. The vibration dose value (VDV) method is used to analyze the rider’s comfort. A comparison is made between the internal combustion engine-based and electric-based two-wheeler to determine its comfort level at the footrest. It is found that the VDV as well as the RMS acceleration decreased considerably in the case of the electric two-wheeler when compared to the internal combustion engine-based vehicle. However, it is found that as the speed is increased, the vibrations increased as well. Hence, further scope is found for the improvement and inculcation of vibration damping at the locations where the vibrations are pronounced in order to improve the overall riding experience of a two-wheeler rider. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 3829 KB

Open AccessProceeding Paper

Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks

by Prabu S., Maheswari M., Jothi B., Banupriya J. and Garikapati Bindu

Eng. Proc. 2023, 59(1), 75; https://doi.org/10.3390/engproc2023059075 - 19 Dec 2023

Cited by 2 | Viewed by 1649

Abstract

Non-geographic routing protocols are inefficient when applied to large-scale mobile networks composed of hundreds of nodes. On the other hand, geographic routing protocols have the disadvantage of needing a location sensor. The goal is to address the challenges of efficient content retrieval and [...] Read more.

Non-geographic routing protocols are inefficient when applied to large-scale mobile networks composed of hundreds of nodes. On the other hand, geographic routing protocols have the disadvantage of needing a location sensor. The goal is to address the challenges of efficient content retrieval and routing scalability in NDN-based networks by leveraging the benefits of both NDN and Bloom Filter technologies. In this article we propose a routing protocol for mobile networks, which is scalable to networks composed of hundreds of nodes. The protocol does not require any localization equipment and is adapted for devices with limited memory and/or processing resources. This goal is achieved through the use of Bloom Filters to efficiently store and spread topological information. In the methodology followed, nodes do not forward messages with topological information to other nodes. To make the process efficient, each node aggregates the topological information it receives from its direct neighbors with its own and only the result of this operation is transmitted to the remaining nodes. Several simulations were carried out in the Qualnet network simulator in order to validate the algorithm proposed by the Hybrid Routing Algorithm with NDNs (HRAN). The obtained results were compared with other non-geographic protocols for mobile networks. HRAN seems to be a routing protocol designed for MANETs, utilizing Bloom Filters to manage topological information. A Bloom Filter is a data structure used to test whether an element is a member of a set. It uses a bit array and multiple hash functions to determine if an element is present in the set. This type of data structure allows storing a large amount of binary information in an efficient way, reducing the resources required by the routing protocol. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 505 KB

Open AccessProceeding Paper

Preparation and Characterization of Activated Carbon Using Pinecone (Conifer Cone) to Remove Phenol from Wastewater

by Lakshmanan Vaidhyaraman, Samuel Peter Lobo and Chikmagalur Raju Girish

Eng. Proc. 2023, 59(1), 76; https://doi.org/10.3390/engproc2023059076 - 20 Dec 2023

Cited by 2 | Viewed by 2360

Abstract

Chemical industries are generating unprecedented effluent, including toxic aromatic compounds such as phenol, which poses severe environmental risks. This study explores the acute and prolonged effects of phenol, which range from the death of animals, birds, and fish to reduced plant growth, reproductive [...] Read more.

Chemical industries are generating unprecedented effluent, including toxic aromatic compounds such as phenol, which poses severe environmental risks. This study explores the acute and prolonged effects of phenol, which range from the death of animals, birds, and fish to reduced plant growth, reproductive problems, and changes in appearance and behaviour. Additionally, oral exposure to phenol can be toxic to humans. Meanwhile, the agricultural sector faces challenges in finding salvage value for increasing amounts of waste. To address this issue, our research analyzes organic materials with no market value and investigates the feasibility of achieving efficient adsorption using their char. We specifically examine pine nuts, an abundantly available waste material. Our objective is to synthesize an organic adsorbent material that meets specific criteria: organic, readily available at zero cost, derived from waste with no other utility, native to the area, abundantly accessible, possessing a large surface area, and demonstrating superior adsorption capabilities. This research employs chemical activation using four acids (nitric acid, sulfuric acid, hydrochloric acid, and orthophosphoric acid) and involves drying and heating the samples at different elevated temperatures. The selection of the optimal adsorbent is based on an analysis of the BET (Brunauer–Emmett–Teller) surface area and pore volume, ensuring its efficacy. The adsorption efficiency was also tested with the help of a UV spectrophotometer to assess its efficiency using Beer–Lambert’s law. The study also goes through an ultimate analysis to measure the amount of carbon content in our adsorbent. Through this study, we aim to develop sustainable waste management practices by utilizing pine nut waste as a valuable resource for effective phenol removal. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1127 KB

Open AccessProceeding Paper

Heuristic-Driven Approach for Efficient Workflow Scheduling in Infrastructure as a Service Using Hybrid Optimization Algorithms

by Sarvesh Kumar, Anubha Jain and Astha Pareek

Eng. Proc. 2023, 59(1), 77; https://doi.org/10.3390/engproc2023059077 - 19 Dec 2023

Cited by 2 | Viewed by 1352

Abstract

The recent trend of Infrastructure as a Service is a service that provides IT components, like computing and storage, on a pay-as-you-go basis over the web. Today, IaaS has endless applications related to the businesses using it. After conducting a contextual analysis, we [...] Read more.

The recent trend of Infrastructure as a Service is a service that provides IT components, like computing and storage, on a pay-as-you-go basis over the web. Today, IaaS has endless applications related to the businesses using it. After conducting a contextual analysis, we note that organizations have moved most of their activities to the cloud. For the most part, this implies that they presently use Software as a Service (SaaS) applications rather than authorized on-prem applications and that they have moved their restrictive programming and frameworks from a server farm to IaaS providers. For years, cloud experts have discussed whether there is truly such an amazing concept as a confidential cloud in IaaS, that is, an on-premises cloud in the client’s server farm. IaaS has undergone an extensive transformation from conventional equipment server farms to a virtualized and cloud-based framework. By eliminating the connection between equipment and working programs and middleware, companies found that they could scale data requirements rapidly and effectively to fulfill their responsibilities. To utilize IaaS, a business can buy a particular resource from a cloud computing supplier to reorganize its computing framework so clients can concentrate on tasks like acquiring and overseeing their own computer programs. This involves incorporating things like computer servers, applications for websites, and versatile gadgets. Along these lines, an enterprise can organize its own equipment infrastructure while having the required assets to carry out a plan of action. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1879 KB

Open AccessProceeding Paper

An Innovative Intrusion Detection System for High-Density Communication Networks Using Artificial Intelligence

by G. Sirisha, K. Vimal Kumar Stephen, R. Suganya, Jyoti Prasad Patra and T. R. Vijaya Lakshmi

Eng. Proc. 2023, 59(1), 78; https://doi.org/10.3390/engproc2023059078 - 19 Dec 2023

Cited by 4 | Viewed by 2633

Abstract

The emergence of Machine Learning (ML) strategies within the scope of community security has led to principal advances in improving clever Artificial Intelligence (AI) primarily based on intrusion detection structures. Intrusion Detection Systems (IDSs) are used to locate malicious conduct in conversation systems [...] Read more.

The emergence of Machine Learning (ML) strategies within the scope of community security has led to principal advances in improving clever Artificial Intelligence (AI) primarily based on intrusion detection structures. Intrusion Detection Systems (IDSs) are used to locate malicious conduct in conversation systems and the internet. A smart AI-based IDS comprises some additives that enable it to provide an automatic and green safety solutions for high-density verbal exchange structures. Present IDS stumble on intrusions and anomalies that are primarily based on predefined guidelines and signature patterns, whereas clever AI primarily based on IDS uses ML methods to gather significant volumes of information from both external and internal sources to hit upon anomalies that could imply a safety breach. Smart AI-based total IDS combines diverse ML methods which are inclusive of supervised studying, unsupervised learning, deep studying, neural networks, and reinforcement-gaining knowledge to create a holistic security solution. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1493 KB

Open AccessProceeding Paper

Review of Development and Characterisation of Shape Memory Polymer Composites Fabricated Using Additive Manufacturing Technology

by Vijay Tambrallimath, Ramaiah Keshavamurthy, Abhinandan Badari, Gagan Raj and Pradeepkumar G. S.

Eng. Proc. 2023, 59(1), 79; https://doi.org/10.3390/engproc2023059079 - 19 Dec 2023

Cited by 5 | Viewed by 2686

Abstract

Structures as well as components are generated by depositing filaments on one another via the technique of additive manufacturing. Among the various processes of printing, 4D printing combines the technology of 3D printing with the passage of time, resulting in additively generated parts [...] Read more.

Structures as well as components are generated by depositing filaments on one another via the technique of additive manufacturing. Among the various processes of printing, 4D printing combines the technology of 3D printing with the passage of time, resulting in additively generated parts that are responsive to stimuli from the outside via modifications of their form, volume, size, or mechanical qualities. Thus, the materials of shape memory are used in 4D printing and respond to environmental factors including temperature, pH, and humidity. Shape memory polymers (SMPs) are materials with a shape memory effect that are best suited for additive manufacturing. Contrarily, the method named fused filament fabrication (FFF) is employed most frequently among all additive manufacturing methods. In this regard, the objective of the present study is to evaluate all investigations that have been conducted on 4D-FFF materials’ mechanical properties. The study offers an unparalleled overview that highlights the possibilities of 4D FFF printing across multiple applications in engineering while keeping the end structure’s or component’s structural integrity in consideration. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1030 KB

Open AccessProceeding Paper

Discount-Based Cloud Resource Management Using Cloud Broker

by M Vinoth Kumar, Medhavi Malik, Suchita Arora, Vinam Tomar, Sunita Pachar and Abhishek Yadav

Eng. Proc. 2023, 59(1), 80; https://doi.org/10.3390/engproc2023059080 - 19 Dec 2023

Viewed by 1351

Abstract

Businesses require ways to check asset use in order not to disregard Service-Level Agreements and guarantee that assets are efficiently distributed to specific departments. A method of allocating, managing, and monitoring cloud resources is provided by cloud resource management systems. They permit one [...] Read more.

Businesses require ways to check asset use in order not to disregard Service-Level Agreements and guarantee that assets are efficiently distributed to specific departments. A method of allocating, managing, and monitoring cloud resources is provided by cloud resource management systems. They permit one to make and oversee pools of assets, allocate those assets to explicit clients or applications, and track how they are being utilized. Users are able to request and provision resources as needed through a self-service interface provided by a good cloud resource management system. When using a cloud provider, businesses that manage their own resources frequently achieve greater efficiency. A portion of the ways in which IT robotization helps organizations deal with their assets involves setting boundaries for the greatest and least number of virtual machines (VMs), setting look-ahead times for VMs to appear, and halting VMs when they are inactive and, at that point, not needed for operations. Moreover, IT organizations might profit from developing a structure of warnings to further develop perceivability and control over asset utilization. Cloud computing is a model used to enable omnipresent, helpful, on-request network admittance to a common pool of configurable processing assets that can be quickly provisioned and delivered with negligible administrative exertion and without specialist organizations. Distributed computing is a financial model for huge corporations, as it removes the requirement for beginning interest in capital or framework costs. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 701 KB

Open AccessProceeding Paper

Dimensionality Reduction Algorithms in Machine Learning: A Theoretical and Experimental Comparison

by Ashish Kumar Rastogi, Swapnesh Taterh and Billakurthi Suresh Kumar

Eng. Proc. 2023, 59(1), 82; https://doi.org/10.3390/engproc2023059082 - 19 Dec 2023

Cited by 14 | Viewed by 8821

Abstract

The goal of Feature Extraction Algorithms (FEAs) is to combat the dimensionality curse, which renders machine learning algorithms ineffective. The most representative FEAs are investigated conceptually and experimentally in our work. First, we discuss the theoretical foundation of a variety of FEAs from [...] Read more.

The goal of Feature Extraction Algorithms (FEAs) is to combat the dimensionality curse, which renders machine learning algorithms ineffective. The most representative FEAs are investigated conceptually and experimentally in our work. First, we discuss the theoretical foundation of a variety of FEAs from various categories like supervised vs. unsupervised, linear vs. nonlinear and random-projection-based vs. manifold-based, show their algorithms and compare these methods conceptually. Second, we determine the finest sets of new features for various datasets, as well as in terms of statistical significance, evaluate the eminence of the different types of transformed feature spaces and power analysis, and also determine the FEA efficacy in terms of speed and classification accuracy. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 845 KB

Open AccessProceeding Paper

A Holistic Approach on Smart Garment for Patients with Juvenile Idiopathic Arthritis

by Choudhary Safal, Randhawa Princy, Kumar J. P. Sampath and H. C. Shiva Prasad

Eng. Proc. 2023, 59(1), 83; https://doi.org/10.3390/engproc2023059083 - 20 Dec 2023

Cited by 2 | Viewed by 2780

Abstract

Juvenile Idiopathic Arthritis (JIA) is a widespread and chronic condition that affects children and adolescents worldwide. The person suffering from JIA is characterized by chronic joint inflammation leading to pain, swelling, stiffness, and limited body movements. Individuals suffering from JIA require ongoing treatment [...] Read more.

Juvenile Idiopathic Arthritis (JIA) is a widespread and chronic condition that affects children and adolescents worldwide. The person suffering from JIA is characterized by chronic joint inflammation leading to pain, swelling, stiffness, and limited body movements. Individuals suffering from JIA require ongoing treatment for their lifetime. Beyond inflammation, JIA patients have expressed concerns about various factors and the lack of responsive services addressing their challenges. The implementation of smart garments offers a promising solution to assist individuals with Juvenile Idiopathic Arthritis in performing their daily activities. These garments are designed to seamlessly integrate technology and clothing, providing not only physical support but also addressing the psychological and emotional aspects of living with a chronic condition. By incorporating sensors, these smart garments can monitor joint movement, detect inflammation, and provide real-time feedback to both patients and healthcare providers. To tackle these comprehensive challenges, the research aims to offer a solution through the design of a smart garment, created with a holistic approach. This smart garment is intended to improve the overall well-being of JIA patients by enhancing their mobility, comfort, and overall quality of life. The integration of technology into clothing can potentially revolutionize the way JIA is managed, allowing patients to better manage their condition and minimize its impact on their daily lives. The synergy between healthcare and technology holds great potential in addressing the multifaceted challenges posed by Juvenile Idiopathic Arthritis patients. Through innovation and empathy, this research aims to pave the way for a brighter future for individuals living with Juvenile Idiopathic Arthritis. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2206 KB

Open AccessProceeding Paper

Study of Different Properties of Graphene Oxide (GO) and Reduced Graphene Oxide (rGO)

by Prateek Viprya, Dhruva Kumar and Suhas Kowshik

Eng. Proc. 2023, 59(1), 84; https://doi.org/10.3390/engproc2023059084 - 20 Dec 2023

Cited by 59 | Viewed by 11853

Abstract

Graphene oxide (GO) and reduced graphene oxide (rGO) are well known for their exceptional characteristics in a variety of applications. Reduced graphene oxide differs from graphene oxide in terms of morphological aspects, quality, functionalized groups, and crystallinities. Several attempts to synthesize GO and [...] Read more.

Graphene oxide (GO) and reduced graphene oxide (rGO) are well known for their exceptional characteristics in a variety of applications. Reduced graphene oxide differs from graphene oxide in terms of morphological aspects, quality, functionalized groups, and crystallinities. Several attempts to synthesize GO and rGO have been documented in studies. The paper discussed the numerous ways to synthesize GO and rGO, and a literature review revealed that Hummers’ technique stands out as the most commonly used. Graphite is mixed with potassium permanganate, sodium nitrate, and strong sulfuric acid to make GO. Notably, Hummers’ technique has the advantage of faster synthesis and higher GO quality. The paper discusses several investigations, including the morphological and structural characteristics, chemical bonding information, and mechanical properties of GO and rGO. Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and the Vickers Hardness Tester are generally used to study these characteristics. The FTIR analysis revealed that the most common peaks in both GO and rGO were found to be associated with the O-H, C=O, C-OH, and C-O functional groups. XRD examination, on the other hand, revealed a diffraction peak at 2θ = 10.2°, indicating oxidized graphite in the case of GO, as well as a graphitic peak at 2θ = 26.3°, indicating graphitic graphite. Furthermore, the addition of GO and rGO into ceramics or polymers was discovered to cause significant changes in their mechanical characteristics, such as tensile strength, Young’s modulus, and others. This demonstrates the revolutionary potential of graphene in improving the performance of composite materials. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2469 KB

Open AccessProceeding Paper

Investigation of Elastic Properties of Sc Doped AlN: A First principles and Experimental Approach

by Jyothilakshmi Rudresh, N. V. Srihari, Suhas Kowshik, Sandeep and K. K. Nagaraja

Eng. Proc. 2023, 59(1), 86; https://doi.org/10.3390/engproc2023059086 - 20 Dec 2023

Cited by 4 | Viewed by 3126

Abstract

Aluminum Nitride (AlN) is a promising piezoelectric material for microelectromechanical systems owing to its attractive physical and chemical properties and CMOS compatibility. It has a moderate piezo response compared to its rival material bound to its wide application. This obstacle can be overcome [...] Read more.

Aluminum Nitride (AlN) is a promising piezoelectric material for microelectromechanical systems owing to its attractive physical and chemical properties and CMOS compatibility. It has a moderate piezo response compared to its rival material bound to its wide application. This obstacle can be overcome by doping or alloying. Sc alloying increases the piezo response of AlN up to four-fold; it also increases the electromechanical coupling coefficient, which is a prominent figure of merit for any MEMS device application. Sc doping induces elastic softening in wurtzite AlN, enhances polarization, and increases piezoelectric constants. However, the possibility of phase separation at higher Sc concentrations, and the wurtzite phase of AlN, which is responsible for piezoelectricity, becomes negligible. Therefore, knowing the optimum concentration of Sc for device applications is necessary. In this work, using density functional theory, we calculated the lattice parameter, band and density of states along with the physical properties such as Young’s modulus, the bulk modulus, Poisson’s ratio, and elastic constants of pristine AlN and Sc doped AlN. The DFT calculations show that the geometrical optimized lattice parameters agree with the literature. As a function of increased Sc concentration, the calculated Young’s modulus and elastic constants decrease, indicating a decrease in hardness and elastic softening, respectively. Meanwhile, the bulk modulus and Poisson’s ratio increase with an increase in Sc concentration, representing an increase in the crystal cell parameters and elastic deformation. AlN and AlScN thin films were grown on Si (111) substrate using magnetron sputtering to study the structural properties experimentally. The deposited films show the required c-axis (002) preferential crystallographic orientation. The XRD peaks of Sc doped AlN thin films have shifted to a lower angle than pristine AlN, indicating elastic softening/tensile stress in grown thin films. So, from our observation, we can conclude that Sc doping induces elastic softening in AlN and deposited films have a preferential crystallographic orientation that can be applied in MEMS devices. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1815 KB

Open AccessProceeding Paper

Enhancing Power Efficiency in 4IR Solar Plants through AI-Powered Energy Optimization

by S. Boobalan, TR. Kalai Lakshmi, Shubhangi N. Ghate, Mohammed Hameeduddin Haqqani and Sushma Jaiswal

Eng. Proc. 2023, 59(1), 87; https://doi.org/10.3390/engproc2023059087 - 19 Dec 2023

Cited by 4 | Viewed by 3164

Abstract

Maximizing the efficiency of solar energy in Industry 4.0 requires an automated AI-powered system. The AI-powered system relies on intelligent algorithms to identify the most efficient energy sources for the industry’s needs and adjust them accordingly while learning from every task it is [...] Read more.

Maximizing the efficiency of solar energy in Industry 4.0 requires an automated AI-powered system. The AI-powered system relies on intelligent algorithms to identify the most efficient energy sources for the industry’s needs and adjust them accordingly while learning from every task it is given. To increase the efficiency of solar energy, the system first utilizes sensors to monitor the environment and conditions of the solar cells, optimizes the solar energy for the machines running in the industrial setting, and then stores the energy in a reserve storage system. By continually monitoring and adjusting the energy settings, the AI-powered system efficiently maximizes the efficiency of the solar energy generated for industrial use. Furthermore, the system can be customized according to your industry’s changing needs and requirements, providing the ability to reduce costs in energy usage while improving the efficiency and productivity of machines. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 4944 KB

Open AccessProceeding Paper

Experimental and Numerical Investigation on Damage Resistance Characteristics of Woven E-Glass/Epoxy Composite Laminates Subjected to Drop-Weight Impacts

by Ranjith K., Prithvi C., Rajesh Mathivanan N. and Rakshith Gowda D. S.

Eng. Proc. 2023, 59(1), 88; https://doi.org/10.3390/engproc2023059088 - 20 Dec 2023

Cited by 3 | Viewed by 2358

Abstract

The utilization of composite materials in structural components has been on the rise in the aerospace, automotive, and marine industries. Although these materials offer numerous benefits, they can be damaged by various sources, such as low-velocity drop-weight impacts. Debris on a runway or [...] Read more.

The utilization of composite materials in structural components has been on the rise in the aerospace, automotive, and marine industries. Although these materials offer numerous benefits, they can be damaged by various sources, such as low-velocity drop-weight impacts. Debris on a runway or tools falling onto composites can cause this type of impact, which has led to extensive research on crashworthiness and impact damage assessment. This study aimed to assess the response of woven E-glass/epoxy composite laminates under low-velocity drop-weight impacts. Tests were conducted using experimental methods and numerical simulations with a drop-weight impact-testing machine and the explicit finite element software LS-DYNA. The experimental tests were performed according to ASTM standards, with varied magnitudes of initial impact energy ranging from 7.85 J to 23.54 J and a specimen thickness of 4 mm. Force–time, energy–time, and force–displacement histories, obtained through the experiments and numerical analyses along with images of the damaged specimens, were examined. The effective stress contours are also illustrated to gain a deeper comprehension of the stress distribution in the laminates. The findings demonstrated that the impact energy significantly influences the impact response of the specimens, and both the experimental and numerical analyses yielded similar results, validating the modeling approach for the impact problem in composite materials. The study provides insight into the damage mechanism of woven E-glass/epoxy composite laminates under drop-weight impacts and is expected to contribute to a better understanding of their response in low-velocity drop-weight impact events. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 2076 KB

Open AccessProceeding Paper

Novel and Optimized Efficient Transmission Using Dynamic Routing Technique for Underwater Acoustic Sensor Networks

by Swapna Babu, Bhuvaneswari Subramanian, Sujitha Madhavadhas, Kavitha Ganesan, Manjula Dhandapani and Surendiran Muthukumar Deva

Eng. Proc. 2023, 59(1), 89; https://doi.org/10.3390/engproc2023059089 - 20 Dec 2023

Viewed by 1427

Abstract

Underwater acoustic sensor networks involve deploying sensors underwater in order to establish a wireless network framework aimed at discovering new resources, detecting targets, and monitoring pollution. However, the primary challenge in these networks lies in enhancing energy efficiency and extending the sensor’s lifespan, [...] Read more.

Underwater acoustic sensor networks involve deploying sensors underwater in order to establish a wireless network framework aimed at discovering new resources, detecting targets, and monitoring pollution. However, the primary challenge in these networks lies in enhancing energy efficiency and extending the sensor’s lifespan, as manually recharging batteries deep within the sea or ocean is not feasible. To address this, we have employed a dynamic network model for target sensing. In an effort to enhance the energy, transmission, and overall lifespan of the Underwater Acoustic Sensor Network (UASN), we have devised a Heuristic Search Algorithm called the Multi-population Harmony Search Algorithm. Additionally, a Dynamic Routing Technique has been developed to dynamically determine whether a given set of sensors should operate or enter sleep mode, with the objective of effectively covering the specified targets. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2778 KB

Open AccessProceeding Paper

Impact of Filler Electrodes on Welding Properties of Dissimilar Welded 316L/201 Austenitic Stainless Steels

by Vipin Tandon, Awanikumar P. Patil and Suhas Kowshik

Eng. Proc. 2023, 59(1), 90; https://doi.org/10.3390/engproc2023059090 - 20 Dec 2023

Cited by 5 | Viewed by 1843

Abstract

In this research, the gas tungsten arc welding method was used to join 201 and 316L austenitic stainless steels using various filler electrodes (316L, 309L and 309LMo), resulting in dissimilar welds, and its various properties, namely, microstructural evolution, mechanical behavior and corrosion behavior [...] Read more.

In this research, the gas tungsten arc welding method was used to join 201 and 316L austenitic stainless steels using various filler electrodes (316L, 309L and 309LMo), resulting in dissimilar welds, and its various properties, namely, microstructural evolution, mechanical behavior and corrosion behavior were investigated. The ferrite–austenite solidification mode was attained, and therefore, different types of ferrite (lathy ferrite and skeletal ferrite) were formed in the austenite matrix in all of the filler electrode weldments’ weld zones, however, the variation in content of ferrite was observed. A ferritoscope was used to estimate the ferrite content in the weld zone, and for E316L, E309L and E309LMo filler electrodes, the ferrite number observed were 8.78, 9.05 and 12.69 units, respectively. Hence, the 316L filler electrode exhibited the lowest ferrite content, while the 309LMo filler electrode weldment displayed a higher ferrite content ascribed to the variation in the chemical composition of filler electrodes (different chemical composition of ferrite stabilizer elements, namely, chromium, molybdenum, etc.). Further, the mechanical characteristics, including microhardness and tensile characteristics, were determined to be higher in the 309LMo filler electrode weldment, followed by the 309L and 316L filler electrode weldments, primarily due to the increased ferrite content. All the welds exhibited failure in the ductile mode. Moreover, higher sensitization was observed in the 309LMo filler electrode weldment, with the 309L and 316L filler electrode weldments following suit, which is ascribed to the higher ferrite content. This higher ferrite content resulted in higher interphase regions of ferrite/austenite, thus resulting in higher sensitization. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 3070 KB

Open AccessProceeding Paper

A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm

by Thangimi Swarna Latha, K. Bhanu Rekha and S. Safinaz

Eng. Proc. 2023, 59(1), 91; https://doi.org/10.3390/engproc2023059091 - 20 Dec 2023

Cited by 6 | Viewed by 1434

Abstract

Localization technology is essential for making wireless sensor networks(WSN)’s information processing and information collecting applications actually feasible. The beacon information is made available to the unknown nodes using the route exchange protocol. These data are more useful for determining the coordinates of neighboring [...] Read more.

Localization technology is essential for making wireless sensor networks(WSN)’s information processing and information collecting applications actually feasible. The beacon information is made available to the unknown nodes using the route exchange protocol. These data are more useful for determining the coordinates of neighboring nodes. Consequently, it was discovered that the algorithm for localizing nodes always has a flaw. Consequently, a brand-new metaheuristic termed Bat with simulated annealing is proposed to fix the flaw in the WSN standard node localization technique. The overall effectiveness of identifying the nodes is enhanced as a result of the large reduction in localization errors. The most popular localization estimation methods are the distance vector hop (DV-Hop) technique and approximate point-in-triangulation (APIT), which have high node localization accuracy and simple deployment in real-time environments. The primary benefits and their disadvantages, which give it a slight disadvantage in preference, are presented in this work. Both strategies are compared for their conventional performance and efficiency when combined with the Bat-SA algorithm. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1465 KB

Open AccessProceeding Paper

Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques

by Raghavendra C. Kamath, G. S. Vijay, Ganesha Prasad, P. Krishnananda Rao, Uday Kumar Shetty, Gautham Parameshwaran, Aniket Shenoy and Prithvi Shetty

Eng. Proc. 2023, 59(1), 92; https://doi.org/10.3390/engproc2023059092 - 19 Dec 2023

Cited by 3 | Viewed by 2192

Abstract

In modern manufacturing industries with Industry 4.0 capabilities, the automated identification and classification of machined surfaces based on their texture will play a crucial role. Texture analysis through computer vision, image processing, classification using artificial neural networks (ANN), and various machine learning techniques [...] Read more.

In modern manufacturing industries with Industry 4.0 capabilities, the automated identification and classification of machined surfaces based on their texture will play a crucial role. Texture analysis through computer vision, image processing, classification using artificial neural networks (ANN), and various machine learning techniques have been prominent research areas in recent decade. Tamura features are very popular in selecting optimum textural features from an image, especially in the medical domain. These textural features correspond to human visual perception and play a significant role in identifying and shortlisting the best features from the photographs. Despite the popularity of Tamura features in the medical domain, their usage in extracting the features from machined surface photographs is seldom reported. Hence, the present study investigates the feasibility of using Tamura features to classify machined surface images produced using turning, milling, grinding, and shaping operations in manufacturing. Photographs of the surfaces produced are obtained using smartphone cameras. Further, the photographs are preprocessed and divided into sixteen different portions. Then, Tamura features are extracted and are given as input to ANN, support vector machines (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The result shows that each machine learning (ML) algorithm performs differently while classifying the same set of machined surface images. Amongst the ML algorithms considered in the study, RF classified the photographs of surfaces machined using different machining operations with the highest accuracy. On the other hand, SVM performed poorly. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1263 KB

Open AccessProceeding Paper

Integrating Pedagogical Approaches in the Study of Conic Sections Using Differential Equation and Analysis via Bayesian Inference

by R. Delhibabu, S. Vaithyasubramanian, R. Sundararajan, C. K. Kirubhashankar, K. Vengatakrishnan and Chandu P.M.S.S.

Eng. Proc. 2023, 59(1), 93; https://doi.org/10.3390/engproc2023059093 - 21 Dec 2023

Viewed by 3269

Abstract

In science and technology, the application of mathematics and mathematical modelling is crucial. A more conceptual and axiomatic approach has been taken in developing the narrative from geometry in the enormous history of mathematics. Mathematics is distinct from all other topics due to [...] Read more.

In science and technology, the application of mathematics and mathematical modelling is crucial. A more conceptual and axiomatic approach has been taken in developing the narrative from geometry in the enormous history of mathematics. Mathematics is distinct from all other topics due to its use of theorems, proofs, axioms, corollaries, examples, results, and analysis. Applications of mathematics can be found, among others, in management sciences, biosciences, chemical technology, computer sciences, information technology, and the medical industry. Differentiation and its extensions are among the most frequently used branches in mathematics. Different curves are created when a plane connects with the surface of a cone. They are called conic sections. Conic sections have uses in physics and architecture, among other fields. In this study, differential equations are used to determine the conic section’s type and locate its center. The effectiveness of conventional and innovative teaching strategies is compared using Bayesian inference. The Bayesian method is employed to update the prior assumptions regarding the relative efficacy of the two approaches. Data on student performance in four different types of classes are gathered for the analysis. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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14 pages, 4389 KB

Open AccessProceeding Paper

The Thiadiazole Ring (THD) Is a Building Block for Potential Inhibitors of the SARS-CoV-2 Main Protease (Mpro): Theoretical Look into the Structure, Reactivity, and Binding Profile of Three 1,3,4-THD Derivatives toward Mpro

by Dileep Chikkur Shanthakumar, Lohith Tumakuru Nagarajappa, Bienfait Kabuyaya Isamura, Mofeli Benedict Leoma, Kabelo Phuti Mokgopa, Sridhar Mandayam Anandalwar, Sahana Doreswamy and Srikantamurthy Ningaiah

Eng. Proc. 2023, 59(1), 94; https://doi.org/10.3390/engproc2023059094 - 21 Dec 2023

Cited by 1 | Viewed by 1794

Abstract

Thiadiazole (THD) derivatives are famous for their exceptional chemical properties and versatile biological activities. In this work, we report computational investigations of the structure, reactivity, and binding affinity of three 1,3,4-THD derivatives (THDs) toward the SARS-CoV-2 main protease (Mpro). Hirshfeld surface (HS) analyses [...] Read more.

Thiadiazole (THD) derivatives are famous for their exceptional chemical properties and versatile biological activities. In this work, we report computational investigations of the structure, reactivity, and binding affinity of three 1,3,4-THD derivatives (THDs) toward the SARS-CoV-2 main protease (Mpro). Hirshfeld surface (HS) analyses are carried out in conjunction with topological calculations in the context of the quantum theory of atoms in molecules (QTAIM) and reduced density gradient (RDG) to unravel the nature and magnitude of noncovalent interactions that contribute to maintaining these THDs. The three approaches consistently indicate that the titled THDs are mainly stabilized by weak intramolecular H…H, C-H…π, C-H…N, and N-H..H interactions in their monomeric forms, while their dimers also exhibit intermolecular π…π stacking and T-shaped contacts. In addition, Hirshfeld atomic charges, frontier molecular orbitals (FMOs), Fukui functions, and molecular electrostatic potential (MEP) reveal that the pyrrolic H atom (ring F) and the imidazole N atom (ring E) are the preferred binding sites for nucleophilic and electrophilic attacks, respectively. Finally, docking and molecular dynamics simulations demonstrate the remarkable binding profile of THDs toward the Mpro, which can be related to potential inhibitory activity. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 727 KB

Open AccessProceeding Paper

Estimation of Energy Storage Capability of the Parallel Plate Capacitor Filled with Distinct Dielectric Materials

by Kiran Keshyagol, Shivashankarayya Hiremath, Vishwanatha H. M. and Pavan Hiremath

Eng. Proc. 2023, 59(1), 95; https://doi.org/10.3390/engproc2023059095 - 21 Dec 2023

Cited by 4 | Viewed by 4735

Abstract

In the present work, the behavior of parallel plate capacitors filled with different dielectric materials and having varied gaps between the plates is developed and analyzed. The capacitor model’s capacitance and energy storage characteristics are estimated numerically and analytically. The simulation results of [...] Read more.

In the present work, the behavior of parallel plate capacitors filled with different dielectric materials and having varied gaps between the plates is developed and analyzed. The capacitor model’s capacitance and energy storage characteristics are estimated numerically and analytically. The simulation results of the model developed in the Multiphysics simulation package show that the capacitance of the capacitor decreases with an increase in the gap between the plates. Similarly, energy storage capacity increases with the material’s dielectric constant, with PVDF showing enhanced storage capacity. Further, the results of both analytical and numerical methods were in good agreement. Thus, the developed model was validated. The findings can potentially advance the design and optimization of capacitor-based systems, enabling the development of improved sensors, actuators, and efficient energy storage applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 4139 KB

Open AccessProceeding Paper

Hand Gesture Recognition in Indian Sign Language Using Deep Learning

by Harsh Kumar Vashisth, Tuhin Tarafder, Rehan Aziz, Mamta Arora and Alpana

Eng. Proc. 2023, 59(1), 96; https://doi.org/10.3390/engproc2023059096 - 21 Dec 2023

Cited by 20 | Viewed by 14020

Abstract

Sign languages are important for the deaf and hard-of-hearing communities, as they provide a means of communication and expression. However, many people outside of the deaf community are not familiar with sign languages, which can lead to communication barriers and exclusion. Each country [...] Read more.

Sign languages are important for the deaf and hard-of-hearing communities, as they provide a means of communication and expression. However, many people outside of the deaf community are not familiar with sign languages, which can lead to communication barriers and exclusion. Each country and culture have its own sign language, and some countries have multiple sign languages. Indian Sign Language (ISL) is a visual language used by the deaf and hard-of-hearing community in India. It is a complete language, with its own grammar and syntax, and is used to convey information through hand gestures, facial expressions, and body language. Over time, ISL has evolved into its own distinct language, with regional variations and dialects. Recognizing hand gestures in sign languages is a challenging task due to the high variability in hand shapes, movements, and orientations. ISL uses a combination of one-handed and two-handed gestures, which makes it fundamentally different from other common sign languages like American Sign Language (ASL). This paper aims to address the communication gap between specially abled (deaf) people who can only express themselves through the Indian sign language and those who do not understand it, thereby improving accessibility and communication for sign language users. This is achieved by using and implementing Convolutional Neural Networks on our self-made dataset. This is a necessary step, as none of the existing datasets fulfills the need for real-world images. We have achieved 0.0178 loss and 99% accuracy on our dataset. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1740 KB

Open AccessProceeding Paper

Prediction of the Reaming Torque Using Artificial Neural Network and Random Forest Algorithm: Comparative Performance Analysis

by M. C. Rakshith, Raghavendra C. Kamath and G. S. Vijay

Eng. Proc. 2023, 59(1), 97; https://doi.org/10.3390/engproc2023059097 - 21 Dec 2023

Cited by 1 | Viewed by 1705

Abstract

In any manufacturing setup, reaming operation is always prominent and present because of ever increasing demands for improved quality of the manufactured products. At the same time, new engineering materials make the process challenging. Further, reaming is the highly sought-after operation to achieve [...] Read more.

In any manufacturing setup, reaming operation is always prominent and present because of ever increasing demands for improved quality of the manufactured products. At the same time, new engineering materials make the process challenging. Further, reaming is the highly sought-after operation to achieve specified tolerance for specified applications to satisfy the rising demand for high-quality and precision-engineered products. Hence, accurate prediction of reaming torque is of utmost necessity, as it gives rise to uneven cutting forces, thereby affecting the surface finish of the reamed hole. High torque produces high-cutting forces, resulting in uneven surface finish and oversized holes. In this regard, the ability of traditional statistical tools to identify intricate correlations and patterns in reaming operation data is limited. To overcome these issues, machine learning methods such as the Artificial Neural Network (ANN) provide reliable options. The present study compares the use of ANN and Random Forest to analyze the data from reaming operations to predict the torque and compares it with those of the Random Forest method and the polynomial regression model. The model is trained and tested using a well-structured dataset that includes multiple input parameters (e.g., material, tool radius, and rotation angle) and the related reaming outputs (e.g., torque) in the suggested supervised learning method. An interconnected single layer of artificial neurons is used to create the ANN model. A comparison is made between the ANN and the Random Forest algorithm, a well-liked ensemble learning technique based on decision trees, to assess the performance of the ANN. The same dataset is used to train both ANN and Random Forest algorithms. The result showed that ANN gave better performance when compared to the other models, with testing accuracy of 94.4% and 61% for ANN and Random Forest, respectively. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 906 KB

Open AccessProceeding Paper

Advancements and Implications of Product Service Systems in the Automobile Industry: A Comprehensive Review

by Dolly Sharma, Vinod Yadav and Dalip Singh

Eng. Proc. 2023, 59(1), 98; https://doi.org/10.3390/engproc2023059098 - 21 Dec 2023

Cited by 2 | Viewed by 2351

Abstract

This research paper explains how, in the last twenty years, the study and research of product service systems (PSSs) in the automobile sphere has experienced high growth due to its unique approach in enriching customer value and experience, strengthening product competence for both [...] Read more.

This research paper explains how, in the last twenty years, the study and research of product service systems (PSSs) in the automobile sphere has experienced high growth due to its unique approach in enriching customer value and experience, strengthening product competence for both customers and providers, and facilitating improved control and management over the product lifecycle. The review of the literature for the automobile sphere is classified into four categories: (i) numbers of publications per year, (ii) journal-specific publications, (iii) year-wise publications, and (iv) growth of research based on applied techniques. The integration of additional services proves instrumental in improving product design, optimizing operations, and offering innovative new services. Additionally, this paper explains how the product service system (PSS) plays an important role in enriching outcomes from customers, efficiency of the product, and lifecycle management. Its future scope lies in integration and fostering innovative ideas, improved customer service, and performance-based contracts because PSSs can drive long-term relationships, trust, and growth in the industry. The paper emphasizes the importance of user-centered design and innovative business models. The primary objective of this review paper is to provide a comprehensive analysis of product service systems (PSSs) in the automobile industry, focusing on progress, challenges, and opportunities, with the aim of responding to the evolution of consumer behavior through innovative innovations such as digitalization and sustainability integration. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 5108 KB

Open AccessProceeding Paper

An Effective Network Intrusion Detection System Using Recursive Feature Elimination Technique

by Narendra Singh Yadav, Vijay Prakash Sharma, D. Sikha Datta Reddy and Saswati Mishra

Eng. Proc. 2023, 59(1), 99; https://doi.org/10.3390/engproc2023059099 - 21 Dec 2023

Cited by 5 | Viewed by 2425

Abstract

Machine learning is an emerging area in research. Nowadays, researchers are utilizing machine learning across all domains to find optimal solutions. Machine learning facilitates the growth of an intrusion detection system (IDS) in the context of cyber security. These systems are proposed to [...] Read more.

Machine learning is an emerging area in research. Nowadays, researchers are utilizing machine learning across all domains to find optimal solutions. Machine learning facilitates the growth of an intrusion detection system (IDS) in the context of cyber security. These systems are proposed to identify and classify cyber-attacks on the network. However, an exhaustive assessment and performance evolution of various machine learning algorithms remains unavailable. In this study, we introduce a framework designed to nurture a versatile and efficient IDS adept at identifying and categorizing unexpected and evolving cyber threats. This is achieved through the use of Recursive Feature Elimination (RFE). In RFE, the algorithm is run recursively until a selected number of features are identified to enhance efficiency and reduce computational cost. The rapid detection of these attacks can facilitate the identification of potential intruders, and the damage will be lowered. We attained remarkable accuracies, with an average rate between 98% and 99% across all the classifiers and against all four types of attacks. The random forest and decision tree models stood out, each achieving peak accuracies of 99% in both KDD-99 and NSL-KDD Datasets. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1760 KB

Open AccessProceeding Paper

Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images

by Christodoss Prasanna Ranjith, Krishnamoorthy Natarajan, Sindhu Madhuri, Mahesh Thylore Ramakrishna, Chandrasekhar Rohith Bhat and Vinoth Kumar Venkatesan

Eng. Proc. 2023, 59(1), 100; https://doi.org/10.3390/engproc2023059100 - 21 Dec 2023

Cited by 4 | Viewed by 2348

Abstract

Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify [...] Read more.

Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify an image’s representation for analysis and interpretation, making it easier for a computer to comprehend and extract meaningful information from visual data. Adaptive K-means clustering is a variant of the classic K-means clustering algorithm in which the number of clusters (K) is continuously adjusted during the clustering process. Unlike classic K-means, which requires you to choose the number of clusters before executing the algorithm, adaptive K-means identifies the best number of clusters based on the features of the data. The proposed model works as follows. Firstly, pre-processing is performed by acquiring all the input images. Secondly, adaptive k-means clustering is employed for segmentation. Thirdly, important features are automatically extracted from X-ray images by making use of a feature-based image registration technique. Then, the detection of bone fractures is automatically carried out. The results are compared with those of existing studies, and it is observed that this model provides better results. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1315 KB

Open AccessProceeding Paper

Differential Evolution Optimized Non-Orthogonal Multiple Access for Sum Rate Maximization

by Dipinkrishnan Rayaroth, Vinoth Babu Kumaravelu, Helen Sheeba John Kennedy, Kalapraveen Bagadi and Francisco R. Castillo Soria

Eng. Proc. 2023, 59(1), 101; https://doi.org/10.3390/engproc2023059101 - 21 Dec 2023

Cited by 3 | Viewed by 1162

Abstract

Non-orthogonal multiple access (NOMA) is a potential technology to support high network density, while satisfying the quality of service (QoS) demands. To maximize the attainable sum rate of individual users and minimize outage, the power allocation (PA) factors must be optimized. In the [...] Read more.

Non-orthogonal multiple access (NOMA) is a potential technology to support high network density, while satisfying the quality of service (QoS) demands. To maximize the attainable sum rate of individual users and minimize outage, the power allocation (PA) factors must be optimized. In the proposed work, a differential evolution (DE) algorithm is implemented to optimize the power factors assigned to users. The proposed optimization maximizes the sum rate by

\approx 5.87 %

to

\approx 12.65 %

compared to random PA. The near user requires

\approx 8.24 d B m

to

\approx 14.13 d B m

less transmit power, whereas the cell-edge user requires

\approx 6.56 d B m

to

\approx 9.18 d B m

less transmit power compared to random PA to attain an outage probability of

10^{- 3}

. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2006 KB

Open AccessProceeding Paper

RETRACTED: Unveiling the Neural Mirage in the Pursuit of Transcendent Intelligence

by Pankaj Sarsia, Satyam Mishra, Aradhya Joshi, Amit Agrawal and Shristi Mishra

Eng. Proc. 2023, 59(1), 102; https://doi.org/10.3390/engproc2023059102 - 21 Dec 2023

Cited by 2 | Viewed by 1863 | Retraction

Abstract

The paper proposes a new approach to AI, called Cognitive Artificial Intelligence (CAI), which is inspired by the human brain. CAI aims to develop AI systems that can think and act rationally, closely mirroring human cognition. The proposed approach, called Knowledge-Expanding System (KES), [...] Read more.

The paper proposes a new approach to AI, called Cognitive Artificial Intelligence (CAI), which is inspired by the human brain. CAI aims to develop AI systems that can think and act rationally, closely mirroring human cognition. The proposed approach, called Knowledge-Expanding System (KES), enables AI systems to acquire profound knowledge and evolve over time, surpassing the intellectual capacities of even the most gifted human minds. The realization of Transcendent Intelligence in AI systems has profound implications for humanity, necessitating thoughtful consideration of ethical aspects and the responsible development and deployment of Transcendent AI for the benefit of humanity. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2004 KB

Open AccessProceeding Paper

An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems

by Somanchi Hari Krishna, Abhiruchi Passi, Vinitha Kanaka, Ishwarya Kothandaraman and Thirumala Reddy Vijaya Lakshmi

Eng. Proc. 2023, 59(1), 103; https://doi.org/10.3390/engproc2023059103 - 22 Dec 2023

Cited by 2 | Viewed by 2112

Abstract

The 5G communication systems are rapidly becoming integral in numerous areas, such as user experience, productivity, and performance, due to increased bandwidth, lower latencies, and superior signal coverage. As such, ensuring a high-performance 5G network has become more important than ever before. To [...] Read more.

The 5G communication systems are rapidly becoming integral in numerous areas, such as user experience, productivity, and performance, due to increased bandwidth, lower latencies, and superior signal coverage. As such, ensuring a high-performance 5G network has become more important than ever before. To this end, different performance metrics, such as throughput, latency, and packet error rate, must be measured and monitored on a regular basis. Time series analysis has emerged as a promising tool to measure, diagnose, and predict the performance of 5G communication systems. By considering the time dimension of metrics such as latency, throughput, and packet error rate, time series analysis provides a comprehensive view of the system and can potentially uncover patterns that are otherwise hidden in isolated metrics. Moreover, this type of analysis can also be used to fine-tune system parameters to improve system performance, detect faults, and identify trends in the system. In this way, time series analysis is an ideal tool for understanding, optimizing, and maintaining 5G communication systems. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1460 KB

Open AccessProceeding Paper

Power-Yeoh: A Yeoh-Type Hyperelastic Model with Invariant I₂ for Rubber-like Materials

by Subraya Krishna Bhat and Keerthan A.

Eng. Proc. 2023, 59(1), 104; https://doi.org/10.3390/engproc2023059104 - 21 Dec 2023

Cited by 4 | Viewed by 4019

Abstract

Rubber-based materials play an important role in various engineering and healthcare applications. Numerous hyperelastic models have been proposed in the long line of literature to model these nonlinear elastic materials. Due to the need to balance simplicity with accuracy, purely invariant I₁ [...] Read more.

Rubber-based materials play an important role in various engineering and healthcare applications. Numerous hyperelastic models have been proposed in the long line of literature to model these nonlinear elastic materials. Due to the need to balance simplicity with accuracy, purely invariant I₁-based models have been proposed, which possess certain limitations with respect to the accurate description of their mechanical behaviors. In this paper, we improve the Yeoh model, a classical and popular I₁-based hyperelastic model with high versatility. The Yeoh model is modified by adding a generalized power-law type term. The model’s capabilities are analyzed under homogeneous deformation modes, such as uniaxial tensile, biaxial tensile and pure shear loading conditions. Experimental data pertaining to rubber-based materials are applied to the proposed hyperelastic model. Also, the interesting phenomenon of thin balloon expansion is investigated by applying the model to relevant experimental data on elastomeric balloons available in the literature. A genetic algorithm-based least squares optimization routine is carried out to determine the material constants while applying the reported experimental data. The results of curve fitting to experimental data pertaining to rubber-based materials showed the capability of the model to describe such multiaxial loading responses with acceptable accuracy (R² ≥ 0.95). The model also showed the capability to describe both the limit-point instability and the strain stiffening in thin rubber balloons, demonstrating its versatility and suitability for modeling rubber-like materials under various applications. The model’s performance can be further extended in the future by coupling terms related to anisotropy, compressibility, damage, etc., according to requirements. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1557 KB

Open AccessProceeding Paper

The Intelligent Connection Management Model to Enhance the Security of Cloud Computers in High-Density Fog Networks

by Archana Jenis Marianthony Renjitham, Suganthi Subburaj, Ariputhran Durasamy Chandramohan Navin Dhinnesh, Jeyasekaran Jeno Jasmine and Raja Ambethkar Matta

Eng. Proc. 2023, 59(1), 105; https://doi.org/10.3390/engproc2023059105 - 22 Dec 2023

Viewed by 2015

Abstract

The Cloud-Based Secured Connection Management Model (CS-CMM) for high-density fog networks is a novel approach that leverages cloud resources and the proliferation of computing power at the edge of networks. The model seeks to address the challenges encountered when managing large FoNets of [...] Read more.

The Cloud-Based Secured Connection Management Model (CS-CMM) for high-density fog networks is a novel approach that leverages cloud resources and the proliferation of computing power at the edge of networks. The model seeks to address the challenges encountered when managing large FoNets of numerous devices. The proposed model uses encrypted and secure connections between devices and the cloud infrastructure. This allows for comprehensive and secure management of nodes, devices, and links. The proposed model utilizes shared communication channels to allow for optimal utilization of connectivity resources, and to reduce the latency of communication. The model also utilizes secure protocols for distributed computing and secure communication, ensuring end-to-end security for all nodes. The proposed model employs self-organizing algorithms and adaptive techniques to enable rapid adaptation to changes in network density and topology. This model provides a secure, efficient, and reliable means of managing high-density fog networks. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1686 KB

Open AccessProceeding Paper

Driving the Energy Transition: Large-Scale Electric Vehicle Use for Renewable Power Integration

by Pankaj Sarsia, Akhileshwer Munshi, Fiza Sheikh, Kavita Yadav and Pushpanjali Shukla

Eng. Proc. 2023, 59(1), 106; https://doi.org/10.3390/engproc2023059106 - 22 Dec 2023

Cited by 6 | Viewed by 3878

Abstract

The global energy shift towards sustainability and renewable power sources is pressing. Large-scale electric vehicles (EVs) play a pivotal role in accelerating this transition. They significantly curb carbon emissions, especially when charged with renewable energy like solar or wind, resulting in near-zero carbon [...] Read more.

The global energy shift towards sustainability and renewable power sources is pressing. Large-scale electric vehicles (EVs) play a pivotal role in accelerating this transition. They significantly curb carbon emissions, especially when charged with renewable energy like solar or wind, resulting in near-zero carbon footprints. EVs also enhance grid flexibility, acting as mobile energy storage, stabilizing power supply. Integrating EVs into renewable systems offers demand response programs, optimizing energy use. However, extensive infrastructure development, particularly charging networks, is a significant challenge. Collaboration among governments, utility companies, and private sectors is crucial to ensure a smooth transition to electric mobility. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1500 KB

Open AccessProceeding Paper

A Novel Information Security Framework for Securing Big Data in Healthcare Environment Using Blockchain

by Lakshman Kannan Venugopal, Rajappan Rajaganapathi, Abhishek Birjepatil, Sundararajan Edwin Raja and Gnanasaravanan Subramaniam

Eng. Proc. 2023, 59(1), 107; https://doi.org/10.3390/engproc2023059107 - 22 Dec 2023

Cited by 9 | Viewed by 2628

Abstract

The Blockchain-based information security framework for health care big data environments is a framework designed for the secure storage, access, and transmission of health care data in big data environments. It combines the privacy and security advantages of encryption and decentralized networks offered [...] Read more.

The Blockchain-based information security framework for health care big data environments is a framework designed for the secure storage, access, and transmission of health care data in big data environments. It combines the privacy and security advantages of encryption and decentralized networks offered by Blockchain technology with the scalability of distributed systems to provide an effective secure platform for big data applications. The framework is based on the principles of confidentiality and immutability to ensure the security and privacy of health care data. The framework is designed to support a wide range of information sources and use cases including patient records, clinical research, medical imaging, genomic data, and pharmaceutical trials. It is also designed to be compatible with existing distributed computing and data querying technologies such as Hadoop and Spark, which will help organizations to improve the accessibility of health care data. The Blockchain-based framework will also provide an audit trail, allowing hospitals and other organizations to better monitor and control access to their data. This will enable organizations to ensure compliance with HIPAA and other regulations, while providing enhanced confidentiality and privacy to users and patients. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2744 KB

Open AccessProceeding Paper

Electrochemical Sensor for Ultra-Sensitive Detection of Lead (II) Ions in Water Using Na₃BiO₄-Bi₂O₃ Mixed Oxide Nanostructures

by Sandeep Gupta, Monika Tripathi, Shikha Sharma and Manoj Kumar

Eng. Proc. 2023, 59(1), 108; https://doi.org/10.3390/engproc2023059108 - 24 Dec 2023

Cited by 3 | Viewed by 1539

Abstract

This study aimed to detect trace amounts of lead using Na₃BiO₄-Bi₂O₃ mixed oxide nanostructures. Scanning electron microscopy (SEM) showed the presence of nanoplates with an average thickness of 90 nm. X-ray diffraction (XRD indicated the presence [...] Read more.

This study aimed to detect trace amounts of lead using Na₃BiO₄-Bi₂O₃ mixed oxide nanostructures. Scanning electron microscopy (SEM) showed the presence of nanoplates with an average thickness of 90 nm. X-ray diffraction (XRD indicated the presence of poly-crystalline Na₃BiO₄ and Bi₂O₃ in the ratio 1:4. The chemical structure of the prepared samples was also studied through X-ray photoelectron spectroscopy. These nanostructured electrodes are highly sensitive to Pb²⁺ ions with a limit of detection of 68 ppt (0.32 nM). Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1115 KB

Open AccessProceeding Paper

Bending Stresses in Profile Corrected Gears

by Sachidananda Hassan Krishnamurthy and Raghunandana Kurkal

Eng. Proc. 2023, 59(1), 109; https://doi.org/10.3390/engproc2023059109 - 24 Dec 2023

Cited by 1 | Viewed by 1816

Abstract

In the present investigation, bending stress of a profile corrected altered tooth-sums gear train, for a constant center distance, is estimated. The number of teeth altered by ±4% is considered and bending stress is estimated for 25° and 20° pressure angle gears. Since [...] Read more.

In the present investigation, bending stress of a profile corrected altered tooth-sums gear train, for a constant center distance, is estimated. The number of teeth altered by ±4% is considered and bending stress is estimated for 25° and 20° pressure angle gears. Since the stress concentration depends on the type of fillet radius, in this work, the bending stresses are computed in the tooth for various fillet radii generated by rack cutters such as; sharp corner tip, rounded corner tip, protuberance tip, and fully rounded tip. It is found that the bending stress is less in the tooth radius generated by a fully rounded tip cutter. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 561 KB

Open AccessProceeding Paper

Comparative Evaluation of the Microleakage of Two Bonding Systems Pretreated with Chitosan Nanoparticles and Restored with Composite Resin: An In Vitro Study

by Aleena Ann Thomas, Neetha Shenoy, Sandya Kini, Krishnaraj Somayaji, Asiya Mujawar, Vivek Hegde and Shahsirashmi Acharya

Eng. Proc. 2023, 59(1), 110; https://doi.org/10.3390/engproc2023059110 - 23 Dec 2023

Cited by 2 | Viewed by 1559

Abstract

Class II cavity preparation was conducted on 84 recently extracted premolars. Group I consisted of teeth restored with composite and without adhesive, (n = 12). Group II consisted of teeth restored with composite using prime and bond universal adhesive (n = [...] Read more.

Class II cavity preparation was conducted on 84 recently extracted premolars. Group I consisted of teeth restored with composite and without adhesive, (n = 12). Group II consisted of teeth restored with composite using prime and bond universal adhesive (n = 36). Group III teeth were restored with composites with Scotchbond universal adhesive (n = 36). Group II and III (n = 18 each) were further subdivided into chitosan pretreated and non-pretreated groups and named as II a, II b, III a, and III b, respectively. Microleakage was tested using the fluid filtration model. The mean microleakage was least for the prime and bond universal groups pretreated with chitosan (0.00145 and 0.00205) at both time periods. This was followed by the Scotchbond universal group pretreated with chitosan group (0.00149 and 0.00203). This was followed by the prime and bond without pretreatment with chitosan groups (0.00229 and 0.00225). Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 590 KB

Open AccessProceeding Paper

Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention

by Vikas R. Shetty, Pooja R. and Rashmi Laxmikant Malghan

Eng. Proc. 2023, 59(1), 111; https://doi.org/10.3390/engproc2023059111 - 25 Dec 2023

Cited by 11 | Viewed by 5158

Abstract

The proliferation of internet services in various industries, especially the financial sector, has increased financial fraud. Fraud detection and prevention are critical to protecting both individuals and organizations from significant financial loss. However, the lack of publicly available datasets containing fraud is a [...] Read more.

The proliferation of internet services in various industries, especially the financial sector, has increased financial fraud. Fraud detection and prevention are critical to protecting both individuals and organizations from significant financial loss. However, the lack of publicly available datasets containing fraud is a major challenge. This study aims to address these issues using advanced machine learning techniques. Known for their ability to provide insight into data, decision trees are used for real-time fraud detection. In addition, deep learning techniques and artificial neural networks (ANN) are used to detect complex fraud patterns, while logistic regression is used to model the probability of fraudulent events. The accuracy of these methods, including decision trees, logistic regression, and ANN, is fully evaluated, with accuracies of 99.8%, 99.9%, and 99.94%, respectively. These findings provide valuable guidance for companies on choosing effective anti-fraud strategies and shed light on the adaptability of algorithms to real financial contexts, contributing to machine learning-based fraud detection. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 957 KB

Open AccessProceeding Paper

A Hybrid MCDM-Grey Wolf Optimizer Approach for Multi-Objective Parametric Optimization of μ-EDM Process

by Partha Protim Das

Eng. Proc. 2023, 59(1), 112; https://doi.org/10.3390/engproc2023059112 - 23 Dec 2023

Cited by 2 | Viewed by 1448

Abstract

Micro-electrical discharge machining (μ-EDM) has come up as an effective material removal process for the manufacturing of miniaturized components in modern industries. The performance and quality of the μ-EDM process mainly depend on the combination of process parameters selected. This paper attempts to [...] Read more.

Micro-electrical discharge machining (μ-EDM) has come up as an effective material removal process for the manufacturing of miniaturized components in modern industries. The performance and quality of the μ-EDM process mainly depend on the combination of process parameters selected. This paper attempts to demonstrate the applicability of three well-known multi-criteria decision-making (MCDM) techniques, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), multi-attributive border approximation area comparison (MABAC), and complex proportional assessment (COPRAS) methods, separately hybridized with the grey wolf optimization (GWO) algorithm. The proposed hybrid optimization approaches are applied to find the optimal parametric setting of a μ-EDM process during machining on a stainless steel shim as the work material. Feed rate, capacitance, and voltage were selected as the machining control parameters, while material removal rate, surface roughness, and tool wear ratio were selected as the responses. The polynomial regression (PR) meta-models are observed as the inputs to these hybrid optimizers. The results obtained are further compared to the traditional weighted sum multi-objective optimization (WSMO) approach, which suggests that all the considered MCDM-PR-GWO approaches outperform traditional PR-WSMO-GWO approaches in obtaining better machining performance measures. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 6823 KB

Open AccessProceeding Paper

Numerical Analysis of Lid-Driven Cavity Flow Induced by Triangular Obstacles

by Sumanth N. Hegde, Nihal L. Bendre and D. Arumuga Perumal

Eng. Proc. 2023, 59(1), 113; https://doi.org/10.3390/engproc2023059113 - 24 Dec 2023

Cited by 2 | Viewed by 3347

Abstract

This research work presents a study on the flow behaviour in the lid-driven cavity (LDC) flows with triangular blocks using computational fluid dynamics techniques. The LDC flow is a widely studied problem that remains a standard for viscous incompressible fluid flows, with a [...] Read more.

This research work presents a study on the flow behaviour in the lid-driven cavity (LDC) flows with triangular blocks using computational fluid dynamics techniques. The LDC flow is a widely studied problem that remains a standard for viscous incompressible fluid flows, with a range of parameters, including the Reynolds number, being explored. The finite volume method was used to discretize the domain, and simulations were computed using ANSYS FLUENT 2021 R1. The fluid flow started when the top wall is moved in the +X direction, whereas the other three walls are kept stationary. A grid independence test was performed to determine the optimum grid size and to obtain a grid-independent solution. Quantitative elements of the 2D flows in lid-driven cavities were explored for Reynolds numbers ranging from 1000 to 8000, and the results were validated against the existing literature. The consequence of different values of the Reynolds number (Re) were analyzed and examined through vorticity, streamline patterns, and kinetic energy contours. The velocity profile at the centerline was enhanced, and the vortex number and size increased with an increase in Re. The behaviour of the isolines of the vortices and the kinetic energy contours was also analyzed. The kinetic energy contours show that the high velocity of the fluid particles close to the upper wall is a significant factor affecting the maximum kinetic energy values. As the Reynolds number increased, the kinetic energy gradually increased at the boundary. This suggests that the Re considerably affects the energy values. Overall, this study provides valuable insights into the flow behaviour of lid-driven cavities and the effects of obstacles on flow patterns, contributing to the existing literature and being useful for researchers and engineers working in the field of fluid dynamics. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1907 KB

Open AccessProceeding Paper

An Innovative Analysis of Time Series-Based Detection Models for Improved Cancer Detection in Modern Healthcare Environments

by Uma Shankari Srinivasan, Venkat Pavithra, Kaliappan Sutha, Sridevi Ramachandiran and Nallathambi Indumathi

Eng. Proc. 2023, 59(1), 114; https://doi.org/10.3390/engproc2023059114 - 25 Dec 2023

Cited by 1 | Viewed by 3254

Abstract

Early detection of cancer is important for successful treatment and improved survival of many cancer types. Technological advances have enabled researchers to develop more precise and reliable methods of cancer detection that go beyond traditional methods, such as biopsy and imaging. Through methods [...] Read more.

Early detection of cancer is important for successful treatment and improved survival of many cancer types. Technological advances have enabled researchers to develop more precise and reliable methods of cancer detection that go beyond traditional methods, such as biopsy and imaging. Through methods such as blood tests, MRI scans, and gene expression profiling, it is now possible to quickly and accurately diagnose many types of cancer. Early detection of cancer can lead to improved outcomes for patients and can even help save lives. Time series analysis is a data mining technique used to identify and analyze the temporal patterns in datasets. The proposed model reached 91.30% accuracy, 90.11% precision, 92.46% recall, and a 90.12% F1-score. This enhanced version of time series analysis incorporates multiple layers of data sources and uses advanced machine learning algorithms to identify patterns that could signal the presence of a tumor. Innovations in time series analysis for cancer detection can have a significant impact on modern healthcare. Time series analysis is a mathematical method used to analyze trends in data over multiple periods. It can be used to identify patterns that may indicate early signs of cancer. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 1320 KB

Open AccessProceeding Paper

An Efficient and Robust Method for Data Privacy and Security on a Public Cloud Using a Novel Hybrid Technique

by Niroshini Infantia Henry, Chinnasamy Anbuananth and Subramanium Kalarani

Eng. Proc. 2023, 59(1), 115; https://doi.org/10.3390/engproc2023059115 - 25 Dec 2023

Cited by 8 | Viewed by 2067

Abstract

The end user has a cost-effective and cloud-based method of storing and retrieving their personal information through remote access using some kind of network connectivity. The user may view the data at any time and from any location they want. However, the data [...] Read more.

The end user has a cost-effective and cloud-based method of storing and retrieving their personal information through remote access using some kind of network connectivity. The user may view the data at any time and from any location they want. However, the data that are stored on the cloud may not always stay in a safe state. As the data can only be accessed by the end user via the intervention of a third party, the authenticity and integrity of the data are at risk of being compromised. It is possible for many people to utilize different Web access at the same time to access and recover their information stored on the cloud. As a consequence, a user’s sensitive data are exposed, leaked, or lost in several locations. Cryptographic methods, such as the Elliptic Curve Cryptography, have been used in the development of a great deal of different algorithms and protocols, all with the intention of preserving the confidentiality and authenticity of the data (ECC). In this research, we present a safe and efficient method for exchanging data via the cloud, while simultaneously preserving both the data’s security and their integrity. The suggested system primarily operates by combining the Elliptic Curve Cryptography (ECC) technique with the Advanced Encryption Standard (AES) method to guarantee verification and maintain the solidarity of data. The findings of the experiments demonstrate that the strategy that was presented is effective and produces superior outcomes when compared to other ways that are already in use. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 978 KB

Open AccessProceeding Paper

Modeling of Barriers to the Adoption of Autonomous Vehicles: DEMATEL Method

by Karan Ashok Jalwani and Shambo Roy Choudhury

Eng. Proc. 2023, 59(1), 116; https://doi.org/10.3390/engproc2023059116 - 26 Dec 2023

Cited by 2 | Viewed by 1851

Abstract

Autonomous vehicles (AVs) have the potential to increase safety while reducing energy use, pollution, and traffic congestion, to name a few positive effects. Industries, however, are having trouble implementing AVs. The goal of this study was to pinpoint and analyze the obstacles preventing [...] Read more.

Autonomous vehicles (AVs) have the potential to increase safety while reducing energy use, pollution, and traffic congestion, to name a few positive effects. Industries, however, are having trouble implementing AVs. The goal of this study was to pinpoint and analyze the obstacles preventing the widespread use of AVs. To do this, a comprehensive review of the literature was conducted to identify the barriers, which were later confirmed by a panel of experts. There were five issues that needed to be addressed: a lack of infrastructure, funding limitations for manufacturing, low customer acceptance, security breach concerns, and potential employment effects. After these barriers were decided upon, the Decision-Making Trial and Evaluation Laboratory method was chosen to model them. The DEMATEL approach makes use of the expertise of groups and experts while relying on matrix tools and graph theory. It develops a visual framework that emphasizes the causal connections between various factors. A DEMATEL digraph is also presented which helps to identify which barrier is the most crucial barrier. Priority ranking was applied to the identified barriers and categorization of barriers was also performed in this study. Two categories were formed, namely, cause and effect barrier categories. Based on the results of DEMMATEL, the lack of funds for manufacturing AVs and the lack of infrastructure are the most crucial barriers to AV adoption. Industries should focus on the cause group barriers first as they run the system. By eliminating cause group barriers, the impact of effect barriers can be reduced. Implications and future directions were provided in the current study. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2307 KB

Open AccessProceeding Paper

Predicting Employee Turnover: A Systematic Machine Learning Approach for Resource Conservation and Workforce Stability

by Parmod Kumar, Sagar Balu Gaikwad, Shunmugavel Thanga Ramya, Tripti Tiwari, Mohit Tiwari and Binod Kumar

Eng. Proc. 2023, 59(1), 117; https://doi.org/10.3390/engproc2023059117 - 26 Dec 2023

Cited by 14 | Viewed by 12434

Abstract

A company’s most valuable resource is its workforce, which includes each worker. Because of the crucial role that employees play in the success of an organization, measuring employee turnover rate has become one of the most important metrics that businesses are concentrating on [...] Read more.

A company’s most valuable resource is its workforce, which includes each worker. Because of the crucial role that employees play in the success of an organization, measuring employee turnover rate has become one of the most important metrics that businesses are concentrating on in the modern era. Attrition may occasionally arise owing to unavoidable circumstances such as moving to a distant place, retirement, etc. But when attrition begins creating holes in the pockets of an organization, it is necessary to monitor the situation closely. When hiring new staff, a company must use a significant quantity of its available resources. The process of rehiring employees needs to be eliminated, and a strong workforce needs to be maintained, so it is necessary to adapt the analysis of systematic machine learning models. From these models, a suitable model that gauges the risk of attrition may then be selected. This not only helps an organization save money by preserving its resources but also assists in preserving the status quo of its staff. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1745 KB

Open AccessProceeding Paper

An Enhanced Automation Analysis for Structural Algorithm in Agro-Industries Using IoT

by Vineetha K R, N. Nagadevi Bala, V. Sudha and D. Balakrishnan

Eng. Proc. 2023, 59(1), 118; https://doi.org/10.3390/engproc2023059118 - 25 Dec 2023

Cited by 4 | Viewed by 1776

Abstract

The Internet of Things (IoT) based structural algorithm for automatic agriculture refers to the system of using powerful real-time data collected from a variety of sensors with software and analytics to autonomously manage agro-ecosystems. This algorithm can be used to monitor environments, analyze [...] Read more.

The Internet of Things (IoT) based structural algorithm for automatic agriculture refers to the system of using powerful real-time data collected from a variety of sensors with software and analytics to autonomously manage agro-ecosystems. This algorithm can be used to monitor environments, analyze data and use this knowledge to take specific actions to help farmers and producers maximize their production and profitability. This algorithm provides an unprecedented level of precision, accuracy and control over the agricultural environment, allowing greater efficiency and optimization in farming practices. It enables monitoring, scheduling, and control of different agro-ecosystem components, such as water, soil, fertilizer, light, humidity, temperature, soil pH and crop growth. The algorithm can also point to general trends and patterns in the environment, as well as offer timely advice to farmers in response to real-time conditions. The algorithm is also capable of automatically diagnosing and responding to unexpected problems, which can help prevent costly mistakes and excessive waste of water, fertilizer, energy, etc. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 6856 KB

Open AccessProceeding Paper

Investigation of Plane-Strain Fracture Toughness and Failure Analysis of AISI-4140 Alloy Steel

by Y. S. Upadhyaya, Rutuja Balaji Kottawar, Narahari Suresh Patil and Vishwanath Managuli

Eng. Proc. 2023, 59(1), 119; https://doi.org/10.3390/engproc2023059119 - 25 Dec 2023

Cited by 3 | Viewed by 3597

Abstract

AISI-4140 alloy steel is a widely used a material in various fields of engineering like automotive, aerospace, oil and gas industries, construction, and structural engineering. Its application in diverse industries is mainly due to its excellent combination of strength, toughness, and wear resistance [...] Read more.

AISI-4140 alloy steel is a widely used a material in various fields of engineering like automotive, aerospace, oil and gas industries, construction, and structural engineering. Its application in diverse industries is mainly due to its excellent combination of strength, toughness, and wear resistance properties. Understanding the fracture toughness of AISI-4140 alloy steel is essential for ensuring the structural integrity and reliability of engineering components made from this material. The current study focuses on the experimental evaluation of fracture toughness due to its critical role in determining the material’s resistance to fracture and crack propagation. In this research, the plane-strain fracture toughness of AISI-4140 alloy steel material is investigated. Five compact tension specimens (CT) with three different thicknesses 12.5 mm, 20 mm, and 25 mm, are prepared as per ASTM standard E 399-90. The initial pre-crack in these specimens is introduced using fatigue loading. The fracture toughness test is conducted as per established procedures, and the specimens are subjected to controlled loading until a fracture occurs. Scanning electron microscopy analysis of the fractured surfaces allowed for a detailed examination of the fracture features, including crack propagation paths and microstructural characteristics. The average fracture toughness value for the AISI-4140 alloy steel is determined as 44.8 MPa

\sqrt{m}

. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 767 KB

Open AccessProceeding Paper

Exploration of Zinc Oxide Nanoparticles for Efficient Photocatalytic Removal of Methylene Blue Dye: Synthesis, Characterization and Optimization

by Montather F. Ramadan, Ashwaq Talib Kareem, Kadhum Al-Majdi and Alaa A. Omran

Eng. Proc. 2023, 59(1), 120; https://doi.org/10.3390/engproc2023059120 - 26 Dec 2023

Cited by 3 | Viewed by 1347

Abstract

Water pollution, particularly through industrial effluents, is a significant environmental challenge. The present study explores the synthesis, characterization, and photocatalytic application of zinc oxide nanoparticles (ZnO NPs) for the degradation of Methylene Blue (MB) dye. ZnO NPs were synthesized via the hydrothermal method, [...] Read more.

Water pollution, particularly through industrial effluents, is a significant environmental challenge. The present study explores the synthesis, characterization, and photocatalytic application of zinc oxide nanoparticles (ZnO NPs) for the degradation of Methylene Blue (MB) dye. ZnO NPs were synthesized via the hydrothermal method, and their structural and morphological features were examined using X-ray diffraction, Transmittance Electron Microscopy, and FESEM techniques. A systematic study was carried out to investigate the effects of catalyst mass dosage, initial dye concentration, and light intensity on photocatalytic degradation efficiency. Results show that the synthesized ZnO NPs are effective in MB dye degradation, and the process adheres to first-order kinetics. This work not only demonstrates the potential of ZnO NPs in addressing industrial dye pollution but also contributes valuable insights toward the development of cost-effective and environmentally sustainable water treatment solutions. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2106 KB

Open AccessProceeding Paper

Experimental Analysis of Feature-Based Image Registration Methods in Combination with Different Outlier Rejection Algorithms for Histopathological Images

by Pritika Adhikari, Bijoyeta Roy, Om Sinkar, Mousumi Gupta and Chitrapriya Ningthoujam

Eng. Proc. 2023, 59(1), 121; https://doi.org/10.3390/engproc2023059121 - 26 Dec 2023

Cited by 2 | Viewed by 1374

Abstract

Registration involves aligning two or more images by transforming one image into the coordinate system of another. Registration of histopathological slide images is a critical step in many image analysis applications including disease detection, classification, and prognosis. It is very useful in Computer-Aided [...] Read more.

Registration involves aligning two or more images by transforming one image into the coordinate system of another. Registration of histopathological slide images is a critical step in many image analysis applications including disease detection, classification, and prognosis. It is very useful in Computer-Aided Diagnosis (CAD) and allows automatic analysis of tissue images, enabling more accurate detection and prognosis than manual analysis. Due to the complexity and heterogeneity of histopathological images, registration is challenging and requires the careful consideration of various factors, such as tissue deformation, staining variation, and image noise. There are different types of registration and this work focuses on feature-based image registration specifically. A qualitative analysis of different feature detection and description methods combined with different outlier rejection methods is conducted. The four feature detection and description methods experimentally analyzed are Oriented FAST and rotated BRIEF (ORB), Binary Robust Invariant Scalable Key points (BRISK), KAZE, and Accelerated KAZE, and the three outlier rejection methods examined are Random Sample Consensus (RANSAC), Graph cut RANSAC (GC-RANSAC), and Marginalizing Sample Consensus (MAGSAC++). The results are visually and quantitively analyzed to select the method that gives the most accurate and robust registration of the histopathological dataset at hand. Several evaluation metrics, the number of key points detected, and a number of inliers are used as parameters for evaluating the performance of different feature detection–description methods and outlier rejection algorithm pairs. Among all the combinations of methods analyzed, BRISK paired with MAGSAC++ generates the most optimal registration results. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2924 KB

Open AccessProceeding Paper

Design and Implementation of Hardware-Implemented Dual-Axis Solar Tracking System for Enhanced Energy Efficiency

by Udit Mamodiya and Neeraj Tiwari

Eng. Proc. 2023, 59(1), 122; https://doi.org/10.3390/engproc2023059122 - 27 Dec 2023

Cited by 11 | Viewed by 4477

Abstract

This paper concentrates on the development of a closed-loop tracking of the sun that precisely follows the sun’s trajectory, allowing photovoltaic panels to capture the maximum amount of solar energy. Azimuthal and elevation-tracking mechanisms are included in the proposed system, and a feedback [...] Read more.

This paper concentrates on the development of a closed-loop tracking of the sun that precisely follows the sun’s trajectory, allowing photovoltaic panels to capture the maximum amount of solar energy. Azimuthal and elevation-tracking mechanisms are included in the proposed system, and a feedback controller based on sensors monitors the brightness of the sun continuously as a reference signal. The controller generates a signal to operate the tracking motor with two axes, orienting the PV panel towards the sun, when the intensity exceeds a set threshold. In both east–west (E-W) and north–south (N-S) directions, the solar tracking system (STS) tracks the sun’s position independently. A dual-axis solar tracking system (DAST) was made of three 335-watt panels (each generating 1 kilowatt of power) in a PV system. Three 335-watt panels were used to successfully execute the dual-axis solar tracking system, with each panel contributing to the PV system’s overall power generation of 1 kilowatt. Overall, the PV system integration of a dual-axis solar tracking system with three 335-watt panels shows the potential for higher power output and energy efficiency. This configuration offers a viable means of maximizing the advantages of renewable energy sources and efficiently harnessing solar energy. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 2051 KB

Open AccessProceeding Paper

Weighted Particle Swarm Optimization Algorithms and Power Management Strategies for Grid Hybrid Energy Systems

by Udayakumar Ramanathan and Sugumar Rajendran

Eng. Proc. 2023, 59(1), 123; https://doi.org/10.3390/engproc2023059123 - 27 Dec 2023

Cited by 5 | Viewed by 1302

Abstract

In independent renewable energy systems (RESs), one of the primary concerns needing to be addressed is the maintaining of power balances between supplies and requirements that are cost-optimized in residences linked to these systems. The amount of power generated through RESs has substantially [...] Read more.

In independent renewable energy systems (RESs), one of the primary concerns needing to be addressed is the maintaining of power balances between supplies and requirements that are cost-optimized in residences linked to these systems. The amount of power generated through RESs has substantially risen, with solar and wind being the two primary sources in RESs. In modern power systems, small-scale distributed networks are growing at a rapid pace and distributed generation (DG) plays an important role. Micro grids are very recent additions to electrical infrastructures. Power management is primarily required for smooth operation, maintaining consistency, and robustness, as well as controlling the actual and reactive power of independent DG. However, the batteries are expensive; moreover, during the charging and discharging process, huge amounts of power are lost, characterizing important problems which have to be averted. This paper introduces the weighted particle swarm optimization (WPSO) method for controlling energy systems and grid hybrid energy systems that comprise photovoltaic (PV), wind turbine, batteries, and diesel generators. By maximizing the power derived from RES and reducing battery power usage, energy is preserved, and the cost of energy consumption (energy of diesel) is reduced. Meteorological data from Spain were used in this study’s simulations. The method depends on the data forecast of renewable energy one day in advance and the everyday load power consumption profile. The results of the simulation show that WPSO outperforms existing algorithms in terms of energies, costs, and battery lives. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 1257 KB

Open AccessProceeding Paper

Sustainable Power Prediction and Demand for Hyperscale Datacenters in India

by Ashok Pomnar, Anand Singh Rajawat, Nisha S. Tatkar and Pawan Bhaladhare

Eng. Proc. 2023, 59(1), 124; https://doi.org/10.3390/engproc2023059124 - 27 Dec 2023

Cited by 1 | Viewed by 4817

Abstract

Data localization, data explosion, data security, data protection, and data acceleration are important driving forces in India’s datacenter revolution, which has raised a demand for datacenter expansion in the country. In addition, the pandemic has pushed the need for technology adoption, digitization across [...] Read more.

Data localization, data explosion, data security, data protection, and data acceleration are important driving forces in India’s datacenter revolution, which has raised a demand for datacenter expansion in the country. In addition, the pandemic has pushed the need for technology adoption, digitization across industries, and migration to cloud-based services across the globe. The launch of 5G services, digital payments, big data analytics, smartphone usage, digital data access, IoT services, and other technologies like AI (artificial intelligence), AR (augmented reality), ML (machine learning), 5G, VR (virtual reality), and Blockchain have been a strong driving force for datacenter investments in India. However, the rapid expansion of these datacenters presents unique challenges, particularly in predicting and managing their power requirements. This abstract focuses on understanding the power prediction and demand aspects specific to hyperscale datacenters in India. The study aims to analyze historical power consumption data from existing hyperscale datacenters in India and develop predictive models to estimate future power requirements. Factors such as server density, workload patterns, cooling systems, and energy-efficient technologies will be considered in the analysis. Datacenter negatively impacts the environment because of the large consumption of power sources and 2% of the global contribution of greenhouse gas emissions. Given the increasing cost of power, datacenter players are naturally encouraged to save energy, as power is a high datacenter operational expenditure cost. Additionally, this research will explore the impact of renewable energy integration, backup power solutions, and demand–response mechanisms to optimize energy usage and reduce reliance on conventional power sources. Many datacenter providers globally have started using power from renewable energy like solar and wind energy through Power Purchase Agreements (PPA) to reduce these carbon footprints and work towards a sustainable environment. In addition, today’s datacenter industry constantly looks for ways to become more energy-efficient through real innovation to reduce its carbon footprint. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1428 KB

Open AccessProceeding Paper

Mapping Sustainable Cities and Communities (SDG 11) Research: A Bibliometric Review

by Dilip Kumar, Abhinav Kumar Shandilya and Sachin George Varghese

Eng. Proc. 2023, 59(1), 125; https://doi.org/10.3390/engproc2023059125 - 27 Dec 2023

Cited by 6 | Viewed by 4573

Abstract

Sustainability is a prime concern in the present scenario, and to achieve the United Nations 2030 agenda, every country is putting in its best effort. Sustainable Development Goal (SDG) 11 explains Sustainable Cities and Communities’ concern for society. Rapid urbanisation and accommodating the [...] Read more.

Sustainability is a prime concern in the present scenario, and to achieve the United Nations 2030 agenda, every country is putting in its best effort. Sustainable Development Goal (SDG) 11 explains Sustainable Cities and Communities’ concern for society. Rapid urbanisation and accommodating the masses, which started shifting from rural to urban society, brought the construction of cities in full swing. This study reviewed the publication and citation trends, sources, trending topics, thematic evolution and thematic map (niche, motor, basic and emerging or declining themes) through metadata extracted from the Scopus database using the Biblioshiny (R-tool) version 4.2.3 open-access software tool. After applying the inclusion and exclusion criteria, only 537 metadata appeared sufficient for the final analysis. This research answered various research questions, an eye-opening lesson in the present scenario, where every country is marching towards achieving the SDG 2030 agenda. Major publications appeared after the launch of the SDG 2030 agenda, i.e., in 2015. Citations also increased after 2016 and reached their peak in 2021 with 1808 citations. Sustainable development goals and sustainability appeared as the most trending topics, whereas, recently, some topics have also emerged which can play a significant role in achieving the SDG 2030 agenda. Various themes such as sustainable development goals, remote sensing, machine learning and Ethiopia have emerged recently that need to be focused on by future researchers to understand this growing concern better, along with the policy development and strict practice approach. More focus is required on the emerging themes, which can be helpful to progress towards SDG 11. More research, and mega-cities with a sustainability approach, can be a milestone in attaining the SDG 11 target. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1929 KB

Open AccessProceeding Paper

Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction

by Babu Kumar, Radhakrishnan Soundararajan, Kanimozhi Natesan and Roobini Maridhas Santhi

Eng. Proc. 2023, 59(1), 126; https://doi.org/10.3390/engproc2023059126 - 27 Dec 2023

Cited by 4 | Viewed by 1637

Abstract

Diseases are major causes for increasing mortality rates. Clinical data analysis must predict cardiovascular disease. Machine learning (ML) may aid decision making and prediction using the healthcare field’s massive data set. ECG demonstrates electrical activities in human hearts, and variations in signals’ morphologies [...] Read more.

Diseases are major causes for increasing mortality rates. Clinical data analysis must predict cardiovascular disease. Machine learning (ML) may aid decision making and prediction using the healthcare field’s massive data set. ECG demonstrates electrical activities in human hearts, and variations in signals’ morphologies have provided improved knowledge of different types of arrhythmia depending on the state of the heart. In order to accurately forecast cardiac disorders, this study effort proposed a hybrid feature selection model and classification together with the ECG wave graph. QRS waves, which are time intervals of binary data, can be determined using the suggested technique of determining the ECG signal’s time interval from R-peak levels to the next level using double squared differences in signals. This approach involves many rounds of data sorting for decreasing noise, thresholding an ECG difference signal by examining the time interval between QRS, and then comparing relative magnitudes to identify the area of interval processing to evaluate accuracy results. In order to choose the best features, a modified chicken swarm optimization algorithm (MCSO) was proposed. Aberrant waves caused by cardiac ailments impacted the dataset patients, according to the suggested research’s unique machine learning methods of multi-module neural network system (MMNNS). The dataset was collected from the ML repository dataset vault at UCI as an individual ECG signal from the Heart Database. The findings demonstrate that each approach has a particular advantage in achieving the aims that have been set out. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 3387 KB

Open AccessProceeding Paper

Fractal-Enhanced Microstrip Antennas: Miniaturization, Multiband Performance and Cross-Polarization Minimization for Wi-Fi Applications

by Sanish Vaipel Sanu, Stephen Rodrigues, Jisha Krishnan Nair Vallikkunnel and Sajitha Anpamattathil Sivan

Eng. Proc. 2023, 59(1), 127; https://doi.org/10.3390/engproc2023059127 - 28 Dec 2023

Cited by 6 | Viewed by 3384

Abstract

Due to the fast advancement of technology and industry, miniaturization has become an important research area. Also, all wired systems are shifting into wireless, and thus, there is a need for antennas to transmit and receive data in and out of gadgets. Fractal [...] Read more.

Due to the fast advancement of technology and industry, miniaturization has become an important research area. Also, all wired systems are shifting into wireless, and thus, there is a need for antennas to transmit and receive data in and out of gadgets. Fractal geometries provide many benefits when used to manufacture microstrip antennas, including features like size filling, multiband, low profile, and compact size. In this study, four fractal antennas, the Sierpinski carpet, Sierpinski gasket, circular patch, and Koch fractal, were designed. Three iterations of the above four antennas were completed. The size of the antennas was 20 mm × 26 mm × 1.6 mm. FR4 epoxy with a full ground was used here for antenna generation. These antennas can be used for 5 GHz band wireless applications. They provide a good return loss at 5.2 GHz. The maximum return loss was achieved using the Koch fractal at its 3rd iteration of −39.85 dB with a gain of 3.6 dB. In order to reduce cross-polarization, a square slot was added in all antennas’ feed lines, and cross-polarization was reduced by up to 60 dB. For simulation purposes, Ansys-HFSS, using FEM for the analysis of complex EM problems, provided accurate results. Also, 3D and 2D radiation patterns were analyzed, and it was found that they were directional in nature with low radiation toward the back side. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 836 KB

Open AccessProceeding Paper

Millennials’ Intention to Visit Green Hotels in India—A Preliminary Analysis Using the Theory of Planned Behavior

by Shikhar Jaitley, Sriram K V and Asish Oommen Mathew

Eng. Proc. 2023, 59(1), 128; https://doi.org/10.3390/engproc2023059128 - 28 Dec 2023

Cited by 5 | Viewed by 2852

Abstract

The purpose of this study is to investigate the factors that impact millennials’ intentions to visit green hotels in India and their willingness to participate in the sustainable practices offered by hoteliers. The main objective is to find antecedents of intentions that drive [...] Read more.

The purpose of this study is to investigate the factors that impact millennials’ intentions to visit green hotels in India and their willingness to participate in the sustainable practices offered by hoteliers. The main objective is to find antecedents of intentions that drive people to visit green hotels using the theory of planned behavior (TPB). This study will focus on millennials as they represent a major part of the consumption economy, having higher disposable income. A preliminary study was conducted using a questionnaire survey, and 35 responses were received. Structural equation modeling was performed to analyze the relationships. Measurement model analysis was performed to validate the instrument and structural model analysis was conducted for hypothesis testing. Collected data were analyzed using SmartPLS V4.0. Findings revealed that subjective norms have a significant impact on customers’ attitude (β = 0.374, p < 0.001) but not much influence on perceived behavioral control (β = 0.218, p > 0.1). Customers’ attitude (β = 0.609, p < 0.001) was found to significantly influence their green purchase intention; PBC (β = 0.242, p > 0.1) did not influence customer’s green purchase intention. Findings also confirmed that attitude is a crucial variable impacting customers’ green purchase intention to visit green hotels. Having a favorable attitude toward saving the environment will have a positive influence on customers’ intention to select a green hotel. This study highlights the factors impacting millennials’ intention to visit green hotels i.e., attitude, subjective norm, and perceived behavioral control. The study’s findings can be put to use by hoteliers to design sustainable strategies to build environmentally viable hotels and create awareness among the millennial generation to contribute towards sustainable tourism. This can also be utilized to gain a competitive advantage for hotels to market them as a differentiating factor in the highly competitive tourism industry. The government can utilize the findings to develop sustainable infrastructure for protecting the environment. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1168 KB

Open AccessProceeding Paper

Strength and Durability Properties of Geopolymer Mortar Made with Concrete Waste Powder

by Pankaj Saini, Paramveer Singh and Kanish Kapoor

Eng. Proc. 2023, 59(1), 129; https://doi.org/10.3390/engproc2023059129 - 29 Dec 2023

Cited by 6 | Viewed by 4216

Abstract

With each passing season, the need for sustainability is growing exponentially. This target of sustainability can only be achieved by innovation in new materials and technologies. Geopolymer binders are innovative materials that can replace cement and play a vital role in attaining sustainability [...] Read more.

With each passing season, the need for sustainability is growing exponentially. This target of sustainability can only be achieved by innovation in new materials and technologies. Geopolymer binders are innovative materials that can replace cement and play a vital role in attaining sustainability in infrastructure. This paper discusses the effective utilization of Concrete Waste Powder (CWP) as a binder to assess the strength and durability properties of geopolymer mortar. Herein a CWP was partially replaced with Ground Granulated Blast Furnace Slag (GGBS) at different replacement levels of 0%, 10%, and 20%. The alkaline solution for geopolymer mortar was made from sodium hydroxide and sodium silicate solutions. For all geopolymer mortar mixes, 0.45 alkali/binder ratio, 12 molarity (M) of sodium hydroxide, 2 of sodium silicate/sodium hydroxide ratio, and 0.35 of water/solid were kept constant. The strength property in terms of compressive strength and durability was accessed in terms of water absorption and porosity of all geopolymer mortar mixes at both ambient and heat curing conditions at 7 and 28 days. The presence of silica, alumina, and calcium in CWP makes it a potential binder for geopolymer mortar. The results suggest that increasing the substitution of GGBS with CWP improves the strength and durability of geopolymer mortar mixes by providing appropriate calcium content along with a geopolymer reaction. The compressive strength increases and water absorption and porosity decrease significantly with a 20% content of GGBS. The utilization of CWP in the production of geopolymer mortar sourced from concrete waste can help to achieve a sustainable environment. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 6033 KB

Open AccessProceeding Paper

Total Harmonic Distortion Analysis of a Seven-Level Inverter for Fuel Cell Applications

by A. S. Veerendra, Punya Sekhar Chavali, R. Shivarudraswamy, C. H. Nagaraja Kumari and Varaprasad Janamala

Eng. Proc. 2023, 59(1), 130; https://doi.org/10.3390/engproc2023059130 - 29 Dec 2023

Cited by 5 | Viewed by 2516

Abstract

This paper focuses on the total harmonic distortion (THD) analysis of a multi-level inverter (MLI) for fuel cell applications. Furthermore, a 50 kW 625 V proton exchange membrane fuel cell (PEMFC) stack was employed for this analysis. The various modes of operation of [...] Read more.

This paper focuses on the total harmonic distortion (THD) analysis of a multi-level inverter (MLI) for fuel cell applications. Furthermore, a 50 kW 625 V proton exchange membrane fuel cell (PEMFC) stack was employed for this analysis. The various modes of operation of the suggested inverter are presented accordingly, along with its switching combinations. Also, a sinusoidal pulse-width modulation (SPWM) controller was employed to drive the power electronic switches in the suggested topology. The suggested inverter can produce sinusoidal voltage with only fundamental frequency switching. Moreover, the number of components and voltage stress of the suggested topology are compared with the conventional topologies presented. In addition, the THD was analyzed with and without the LC filter. Finally, the validity of the system was verified through MATLAB/Simulink software R2022b. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 3631 KB

Open AccessProceeding Paper

Experimental Investigation of Two- and Three-Blade Savonius Hydrokinetic Turbine for Hydropower Applications: A Study across Various Turbine Positions from Channel Centre to Channel Wall

by Shanegowda Tharedakuppe Gangashanaiah, Shashikumar C M, Veershetty Gumptapure and Vasudeva Madav

Eng. Proc. 2023, 59(1), 131; https://doi.org/10.3390/engproc2023059131 - 29 Dec 2023

Cited by 4 | Viewed by 3105

Abstract

Hydrokinetic energy has gained significant attention in recent years as a promising renewable energy source due to its low environmental impact and potential for use in remote locations. This research aims to optimize the performance of the Savonius hydrokinetic turbine, a crucial component [...] Read more.

Hydrokinetic energy has gained significant attention in recent years as a promising renewable energy source due to its low environmental impact and potential for use in remote locations. This research aims to optimize the performance of the Savonius hydrokinetic turbine, a crucial component of zero-head hydropower systems, for efficient renewable energy extraction from flowing water. Laboratory-scale experiments with two and three-blade Savonius turbines at different channel positions investigate geometric dimensions and design parameters like the power coefficient (C_P) and Torque coefficient (C_T). The experimental results are compared with previous research, confirming the superiority of the two-blade configuration, which achieved C_P and C_T at the same TSR and channel locations. Specifically, the two-blade Savonius turbine demonstrated a C_P of 0.27 and a C_T of 0.37 at TSR 0.7 and the channel’s centre placement. Placing the turbine at the channel centre yields the best performance for both configurations. This study provides valuable insights for enhancing the efficiency of hydrokinetic turbines, contributing to renewable energy technology advancements, and addressing climate change and energy security challenges. The Savonius hydrokinetic turbine has the potential to be a sustainable energy source. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 732 KB

Open AccessProceeding Paper

Histopathological Image Analysis Using Deep Learning Framework

by Sudha Rani Vupulluri and Jogendra Kumar Munagala

Eng. Proc. 2023, 59(1), 132; https://doi.org/10.3390/engproc2023059132 - 29 Dec 2023

Cited by 3 | Viewed by 3854

Abstract

Breast cancer has the highest mortality rate. Therefore, histologic imaging evaluations must detect breast cancer early. Traditional methods are time-consuming and limit pathologists’ skills. Breast cancer histopathology picture segmentation is neglected by existing HIAs because of its complexity and lack of historical data [...] Read more.

Breast cancer has the highest mortality rate. Therefore, histologic imaging evaluations must detect breast cancer early. Traditional methods are time-consuming and limit pathologists’ skills. Breast cancer histopathology picture segmentation is neglected by existing HIAs because of its complexity and lack of historical data with exact annotations. Histopathology breast cancer images are classified using graph-based segmentation. Graph-segmented images retrieve relevant features. Using recursive feature removal, breast cancer photographs are categorized. Breast cancer symptoms can be detected by appropriately classifying breast histopathology scans as abnormal or normal. Modern medicine diagnoses and predicts diseases, including cancer, using histopathological image analysis. Due to picture identification and feature extraction, deep learning can automate and improve histopathological image analysis. This study extensively analyses deep learning frameworks in histopathology image analysis. Starting with histopathological image interpretation’s challenges, this study emphasizes the intricate patterns, cell structures, and tissue anomalies that demand professional attention. It then examines CNNs, RNNs, and their variants’ design and ability to catch subtle features and patterns in histopathological images. We examine tumour detection, grading, segmentation, and prognosis using deep learning in histopathology. For each problem, this article evaluates cutting-edge deep learning models and approaches to demonstrate their accuracy and efficiency. While training deep learning models for histopathology image analysis, this study tackles data collection, preprocessing, and annotation. We also analyse automated clinical systems’ ethical and regulatory ramifications. Deep learning-based histopathological image processing case studies show patient care and applications. Multi-modal data fusion, transfer learning, and explainable AI may increase the accuracy and interpretability of histopathological image analyses. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 477 KB

Open AccessProceeding Paper

Biomaterials: A Sustainable Solution for a Circular Economy

by Jayana Rajvanshi, Monika Sogani, Anu Kumar and Sudipti Arora

Eng. Proc. 2023, 59(1), 133; https://doi.org/10.3390/engproc2023059133 - 30 Dec 2023

Cited by 12 | Viewed by 6041

Abstract

A few of the reasons behind the introduction of the concept of biomaterials, such as biopolymers to address problems like plastic pollution, include a lower reliance on fossil resources, reduced carbon emissions, a focus on non-renewable resource allocation, and, most importantly, promoting a [...] Read more.

A few of the reasons behind the introduction of the concept of biomaterials, such as biopolymers to address problems like plastic pollution, include a lower reliance on fossil resources, reduced carbon emissions, a focus on non-renewable resource allocation, and, most importantly, promoting a circular economy. The high production cost is a hindrance in scaling up the production and market for biobased and biodegradable plastics, but microbial production of such monomers or polymers using easily available and inexpensive substrates, like waste streams, can be seen as a potential strategy to overcome this challenge. These polymers, with properties similar to conventional plastics, can be applied as alternatives in different sectors. According to the Intergovernmental Panel on Climate Change (IPCC), climate change now requires immediate mitigation. Looking at this need, the construction industry has started using biobased materials to focus on reducing CO₂ emissions. Similarly, the improved barrier, mechanical, antimicrobial, and antioxidant properties; biocompatibility; and biodegradability of biomaterials like PLA, PHA, etc., make them suitable for various other sectors. The present paper will focus on highlighting the multi-functionality of such biobased materials that will further open many opportunities for significant innovation. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 6252 KB

Open AccessProceeding Paper

Hydrothermal Synthesis of Mesoporous FeTiO₃ for Photo-Fenton Degradation of Organic Pollutants and Fluoride Adsorption

by Neha Gupta, Arpita Sarkar, Bivek Pradhan and Soumya Kanti Biswas

Eng. Proc. 2023, 59(1), 134; https://doi.org/10.3390/engproc2023059134 - 30 Dec 2023

Cited by 2 | Viewed by 2171

Abstract

Metal oxide semiconductor-based photocatalysis and advanced oxidation processes (AOPs) are effective in treating various recalcitrant pollutants such as organic dyes present in industrial wastewater streams. AOPs rely on the highly reactive hydroxyl radicals (OH^•) that facilitate the non-selective destruction of most [...] Read more.

Metal oxide semiconductor-based photocatalysis and advanced oxidation processes (AOPs) are effective in treating various recalcitrant pollutants such as organic dyes present in industrial wastewater streams. AOPs rely on the highly reactive hydroxyl radicals (OH^•) that facilitate the non-selective destruction of most organic pollutants. Here, we present the novel synthesis of mesoporous FeTiO₃ catalyst via a simple, hard template-free, aqueous-solution-based hydrothermal synthesis method. The surfactant, tetradecyltrimethylammonium bromide (TTAB), was used as the structure-directing agent, the removal of which led to the formation of the mesoporous structure. The catalyst was characterized by thermo-gravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), Branauer–Emette–Teller analysis (BET), X-ray diffraction (XRD), and scanning electron microscope (SEM) techniques. The obtained catalyst has been studied for its photocatalytic application in the presence of H₂O₂ towards the degradation of organic dyes as representative pollutants, namely, rhodamine B (RhB) and methylene blue (MB) under direct solar light irradiation. The various characterizations confirm the formation of mesoporous FeTiO₃ with a pore size of ≈7.5 nm and a specific surface area of 65 ± 5 m²/g. The influence of H₂O₂ oxidant on the removal of the said dyes has also been studied at various concentrations in the presence of the synthesized catalyst to determine the optimum dosage of H₂O₂. The catalyst was efficient in the complete synergistic adsorption-led photo-Fenton-like removal of MB in just 30 min of irradiation time, while the 96% RhB was degraded in 240 min. Moreover, this catalyst has also shown potential for fluoride adsorption that reaches up to more than 50% in 90 min. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 6951 KB

Open AccessProceeding Paper

Investigation of Machining Parameters and Surface Quality of AZ-31 Magnesium Alloy Subjected to Spark Machining

by Shouryha Bhardwaj, Arnav Agarwal, Ishriit Tibdewal, Gururaj Bolar and Vishwanath Managuli

Eng. Proc. 2023, 59(1), 135; https://doi.org/10.3390/engproc2023059135 - 31 Dec 2023

Cited by 2 | Viewed by 2091

Abstract

Magnesium alloys are commonly used in various industries such as automotive, aerospace, electrical, medical, sports, etc. The material is preferred in the making of engine blocks, transmission cases, and structural parts due to its unique material properties, like being lightweight and durable. It [...] Read more.

Magnesium alloys are commonly used in various industries such as automotive, aerospace, electrical, medical, sports, etc. The material is preferred in the making of engine blocks, transmission cases, and structural parts due to its unique material properties, like being lightweight and durable. It also offers a good strength-to-weight ratio and directly contributes to the fuel efficiency of vehicles. Due to its usage in various industries, it is essential to understand its behavior under machining. But the machining of magnesium alloys can present significant challenges compared to other conventional structural metals and alloys. The research work focuses on investigating the application of a Plug Electrical Discharge Machine (EDM) for machining the AZ-31 magnesium alloy and aims to analyze the surface quality of the machined surface for selected input parameters. The experiments were conducted on a mirror-finished flat specimen while keeping the incision depth and servo voltage constant at 0.3 mm and 45 V, respectively. A copper tool was used to make nine unique incisions on the surface using selected values of pulse on-time (T_on), pulse off-time (T_off), and current (I). A surface analysis using optical microscopy revealed that the surface roughness increased drastically with a combination of high values of I, T_on, and T_off. The tests conducted using a profilometer confirmed the proportional relationship between the input parameters and the surface roughness of the AZ-31 magnesium alloy. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 2794 KB

Open AccessProceeding Paper

Performance Enhancement of Aged Mineral Oil by Blending Synthetic Ester for Transformer Insulation Applications

by Monika Nagathihalli Hiriyanna, Ashwini Basavaraju, Moulya Ashok Kumar, Gagana Gurunathegowda and Gowrishankar Shanmugam

Eng. Proc. 2023, 59(1), 136; https://doi.org/10.3390/engproc2023059136 - 2 Jan 2024

Cited by 8 | Viewed by 3548

Abstract

Mineral oil derived from petroleum is the most preferred liquid insulation and coolant in power and distribution transformers because of its stability at high temperatures, as well as its excellent electrical insulating properties. Since it is non-biodegradable and inflammable, an alternative insulating liquid [...] Read more.

Mineral oil derived from petroleum is the most preferred liquid insulation and coolant in power and distribution transformers because of its stability at high temperatures, as well as its excellent electrical insulating properties. Since it is non-biodegradable and inflammable, an alternative insulating liquid should be developed, or a possible approach should be established, to further utilize the aged mineral oil in the existing transformers to avoid the problem of discarding the oil. Though natural esters look like a promising alternative, they suffer from excessive oxidation which makes them unsuitable for transformer insulation applications. This work presents the feasibility of blending aged mineral oil with synthetic ester to extend its life. Both fresh and aged mineral oil were blended with synthetic ester separately using ultrasonication after the removal of moisture content. The electrical, thermal, and physiochemical characteristics of the blended oil were studied by measuring breakdown voltage, flash and fire points, and viscosity, respectively. These characteristics varied depending upon the ratios of mineral oil and synthetic ester. The optimum ratio of synthetic ester and mineral oil for enhanced performance was found as 1:4 for both fresh and aged mineral oils. This adds an advantage of reduced synthetic ester requirement and, thus, reduced cost. A comparison of results also revealed that the optimum ratio of mineral oil and synthetic ester depends on the ageing condition of the oil, electrical, physiochemical, and thermal properties of the blended oil. The results also proved that the aged mineral oil can be reused after blending it with synthetic ester, which avoids discarding the oil. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1062 KB

Open AccessProceeding Paper

Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models

by Jayavani Vankara, Sekharamahanti S. Nandini, Murali Krishna Muddada, N. Satya Chitra Kuppili and K Sowjanya Naidu

Eng. Proc. 2023, 59(1), 137; https://doi.org/10.3390/engproc2023059137 - 21 Dec 2023

Cited by 2 | Viewed by 1809

Abstract

There have been several initiatives taken to guarantee higher yields and higher-quality crops as the agriculture sector grows. The agriculture industry is severely impacted by plant and agricultural illnesses and deficits. Several techniques and technologies have been developed to aid in the diagnosis, [...] Read more.

There have been several initiatives taken to guarantee higher yields and higher-quality crops as the agriculture sector grows. The agriculture industry is severely impacted by plant and agricultural illnesses and deficits. Several techniques and technologies have been developed to aid in the diagnosis, management, and eventual eradication of plant diseases. The efficient and accurate identification of plant diseases could be aided by the development of a quick and accurate model. The use of deep convolutional neural networks for image categorization has greatly improved accuracy. In this paper, we present a framework for automating disease detection by the use of a tailored DL architecture. Both the Plant Village dataset and the real-time field dataset are utilized in the testing process. Our model’s results are compared to those of other spatial exploitation models. The results show that the proposed method is superior to the standard deep-learning classifier. This proves the network’s potential for usage in real-time applications by extracting high-level features that boost the efficiency and accuracy while reducing the risk introduced by a manual procedure. In order to enable a prompt reaction, and perhaps a targeted pesticide application, the suggested method has the ability to provide the early diagnoses of plant vital health. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1184 KB

Open AccessProceeding Paper

Performance Evaluation of Various Ni-Based Catalysts for the Production of Hydrogen via Steam Methane Reforming Process

by Sudeep Noorambala Subramanya, Vaka Sai Charan Reddy and Vasudeva Madav

Eng. Proc. 2023, 59(1), 138; https://doi.org/10.3390/engproc2023059138 - 2 Jan 2024

Cited by 3 | Viewed by 4494

Abstract

Steam methane reforming (SMR) approaches are highly recognised and pivotal in industrial H₂ production, contributing over 40% to global hydrogen production. The prime objective of this study is to optimise the significant parameters involved in the SMR process to achieve the utmost [...] Read more.

Steam methane reforming (SMR) approaches are highly recognised and pivotal in industrial H₂ production, contributing over 40% to global hydrogen production. The prime objective of this study is to optimise the significant parameters involved in the SMR process to achieve the utmost conversion of CH₄ to H₂. To attain this, a sophisticated one-dimensional unsteady-state heterogeneous plug flow reactor (PFR) model was methodically constructed and simulated using the Aspen HYSYS V11 software. The study comprises an exhaustive comparison of seven diverse sets of catalysts, primarily categorised based on the different weight percentages of Ni in Ni/Al₂O₃ catalysts, along with various promoters incorporated to enhance the conversion rate in the SMR process. This comprehensive evaluation identifies the most operative catalyst configuration for optimising CH₄ conversion. The results obtained through the simulations revealed that CH₄ conversion intensifies with an increase in temperature, while it weakens with higher pressures within the catalyst set considered for the study. The analysis yielded promising conclusions by comparing the simulated CH₄ conversion percentages at various temperatures with data from the existing literature. The maximum absolute error encountered was only 3.72%, signifying the accuracy and reliability of the developed model. Moreover, the Mean Absolute Error (MAE) calculated was a low 1.42%, suggesting the robustness of the proposed approach. The findings lay the foundation for future innovations and improvements in the field, ultimately fostering more efficient and sustainable hydrogen generation. As the demand for clean energy grows, the optimisation of the SMR process becomes increasingly vital, making this study a crucial step towards meeting global energy needs while minimising environmental impact. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1948 KB

Open AccessProceeding Paper

Efficient Deep Learning-Based Cyber-Attack Detection for Internet of Medical Things Devices

by Abigail Judith, G. Jaspher W. Kathrine, Salaja Silas and Andrew J

Eng. Proc. 2023, 59(1), 139; https://doi.org/10.3390/engproc2023059139 - 24 Dec 2023

Cited by 24 | Viewed by 4425

Abstract

The usage of IoT in the medical field, often referred to as IoMT, plays a vital role in facilitating the exchange of sensitive data among medical devices. This capability significantly contributes to enhancing the quality of patient care. However, it comes with privacy [...] Read more.

The usage of IoT in the medical field, often referred to as IoMT, plays a vital role in facilitating the exchange of sensitive data among medical devices. This capability significantly contributes to enhancing the quality of patient care. However, it comes with privacy issues that compromise the security of the data collected by medical sensors, making them vulnerable to potential cyber threats such as data modification, replay attacks, etc. These attacks can lead to significant data loss or unauthorized alterations. Machine learning, particularly in cyber-attack detection systems, is crucial for identifying and classifying such attacks. Yet, the main challenge lies in adapting to the dynamic and unpredictable nature of malicious attacks and creating scalable solutions to combat them. The objective of this paper is to detect cybersecurity threats, with a particular focus on man-in-the-middle attacks that occur within the IoMT communication network. The study utilizes principal component analysis (PCA) for feature reduction and employs multi-layer perceptron to classify unforeseen cyber-attack IoT-based healthcare devices. The study evaluates the effectiveness of this proposed strategy using real-time data from the St. Louis Enhanced Healthcare Monitoring System (WUSTL-EHMS). The findings indicate that the multi-layer perceptron outperforms other tested classifiers, achieving an accuracy score of 96.39%, while also improving the performance by reducing the time complexity. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 3784 KB

Open AccessProceeding Paper

Optimization of Performance and Emission Responses of Common Rail Direct Injection Engine by Taguchi-Grey Relational Analysis Technique

by B. S. Nithyananda, G. V. Naveen Prakash, Naveen Ankegowda, K. B. Vinay and A. Anand

Eng. Proc. 2023, 59(1), 140; https://doi.org/10.3390/engproc2023059140 - 4 Jan 2024

Cited by 5 | Viewed by 1567

Abstract

India imports fossil fuels to meet its energy needs, and the need is anticipated to increase over the coming years. The constant usage of crude fuel will initiate its depletion in due course, necessitating the hunt for substitute fuels. One of the most [...] Read more.

India imports fossil fuels to meet its energy needs, and the need is anticipated to increase over the coming years. The constant usage of crude fuel will initiate its depletion in due course, necessitating the hunt for substitute fuels. One of the most promising alternatives to fossil fuels is determined to be biofuels. Fuels made from second-generation feedstocks, particularly non-edible oils, may change the game in this situation. The use of Simarouba non-edible oil as a substitute for diesel in common rail direct injection (CRDI) engines is the subject of the current piece of research. Running a CRDI engine with Simarouba biodiesel blends may not be suitable under the same operating conditions as running a diesel engine. To optimize performance, the ideal conditions for operating a CRDI engine with Simarouba biodiesel mixes needs to be discovered. The control settings in this study that affect the engine’s performance viz., fuel temperature (FPT), fuel injection pressure (IP), and injection time (IT) are designed using the Taguchi technique. Under full load conditions of 12 kg, for the biodiesel blends viz., SB5, SB10, and SB20, studies were conducted using the Taguchi L9 orthogonal array (OA). Through experimentation, performance and emission responses were obtained. For analysis, six engine responses were considered. The analysis shows that each response is uniquely impacted by the engine control parameters. Therefore, it might be challenging to pinpoint the ideal circumstances that would improve performance and lower emissions. Thus, this presents an example of a multi-response optimization problem. For this reason, multiple response optimization uses Taguchi-Grey Relational Analysis (TGRA). According to TGRA, the ideal settings to increase engine performance while using Simarouba biodiesel blends as fuel are an FPT of 40 °C, an IT of 21° bTDC, and an IP of 600 bar. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 14003 KB

Open AccessProceeding Paper

Non-Linear Optical Properties for Thin Films of Fluorescein Organic Laser Dyes Doped with Polyvinyl Alcohol Polymer and Al₂O₃ Nanoparticles

by Ban R. Saleh, Ban A. Naser and Zahraa N. Salman

Eng. Proc. 2023, 59(1), 141; https://doi.org/10.3390/engproc2023059141 - 4 Jan 2024

Cited by 7 | Viewed by 2132

Abstract

This article presents a comprehensive study on the optical properties of fluorescein dye doped with PVA polymer and Al

_{2}

O

_{3}

nanoparticles. The investigation covers the material’s absorbance behavior, surface morphology, and nonlinear optical characteristics. Through the use of UV-VIS spectrometry and [...] Read more.

This article presents a comprehensive study on the optical properties of fluorescein dye doped with PVA polymer and Al

_{2}

O

_{3}

nanoparticles. The investigation covers the material’s absorbance behavior, surface morphology, and nonlinear optical characteristics. Through the use of UV-VIS spectrometry and AFM, the study demonstrates how doping affects the dye’s absorption spectrum and grain size on the surface. The application of the Z-scan technique further allows for the measurement of the nonlinear refractive index and absorption coefficient, revealing self-defocusing lensing and distinguishing the absorption phenomena in both solutions and thin films. The results underscore the potential of fluorescein-based materials in the development of advanced optical devices, offering valuable insights for future research in material science and photonics. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 2655 KB

Open AccessProceeding Paper

Analysis and Modeling of 581 kWp Grid-Integrated Solar Photovoltaic Power Plant of Academic Institution Using PVsyst

by Jayalaxmi Rajesh Hanni, Mahipal Bukya, Pancham Kumar and Nagaraju Gowtham

Eng. Proc. 2023, 59(1), 142; https://doi.org/10.3390/engproc2023059142 - 4 Jan 2024

Cited by 8 | Viewed by 2888

Abstract

Solar photovoltaic (PV) technology has become increasingly common in the energy sector in recent years. India has abundant renewable and non-renewable energy sources. India’s average solar insulation is 5000 T kWh per year (or 500 TW). The main objective of this paper is [...] Read more.

Solar photovoltaic (PV) technology has become increasingly common in the energy sector in recent years. India has abundant renewable and non-renewable energy sources. India’s average solar insulation is 5000 T kWh per year (or 500 TW). The main objective of this paper is to present a design for a 581 kWp on-grid solar photovoltaic system at the academic institution using PVsyst software. In our study, works have been carried out to indulge the power losses which occurred due to interconnections, temperature, irradiation, inverter, wiring, soiling, power electronics, and grid availability. The results of our investigation showed the average global horizontal irradiation and PV Plant Performance ratio (PR) were found to be 5.28 kWh/m²/day and 84.14%, respectively. The investigation results of per year production capacity of the proposed plant was found to be 971,271 kWh which is completely CO₂-free. As a result, the cost of electricity at the academic institution was Rs 7,915,858/− less per year. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 7107 KB

Open AccessProceeding Paper

M-Shaped Conformal Antenna with FSS Backing for Gain Enhancement

by Madhavi Devi Lanka and Subbarao Chalasani

Eng. Proc. 2023, 59(1), 143; https://doi.org/10.3390/engproc2023059143 - 4 Jan 2024

Cited by 4 | Viewed by 1979

Abstract

A frequency selective surface (FSS) integrated conformal antenna is modelled and analytical study is presented in this article. A novel antenna design known as the “M-shaped Conformal Antenna with FSS Backing for Gain Improvement” makes use of both the conformal structure and FSS [...] Read more.

A frequency selective surface (FSS) integrated conformal antenna is modelled and analytical study is presented in this article. A novel antenna design known as the “M-shaped Conformal Antenna with FSS Backing for Gain Improvement” makes use of both the conformal structure and FSS technology to increase gain. The geometric shape of the M-shaped antenna, which might resemble the letter “M” or a collection of M-shaped parts, is what gives it its name. This structure can be created to alter the antenna’s resonance frequency, increase bandwidth, or adjust the emission pattern. The radiation pattern of the antenna may be precisely controlled by combining an M-shaped construction with an FSS. You may customize the radiation pattern to concentrate energy in particular directions or sectors, boosting gain and coverage, when necessary, by modifying the FSS’s geometry and physical characteristics. The combination of features makes it extremely ideal for a variety of applications where optimum gain is a crucial need, such as aerospace, communications, and radar arrays. It also enables fine control of the radiation pattern, frequency-selective gain, and interference elimination. The designed antenna consists of an M-shaped model on the visible sideways along with a complement split ring resonator and a defective ground structure on the bottom side. Antenna resonating at wideband cover several lower band wireless communication applications like Bluetooth, Wireless Fidelity (Wi-Fi), Manufacturing Communication and Pharma, Long Term Evolution-LTE, advanced 5G, and Wireless LAN with impedance bandwidth of 65%. The FSS beneath the antenna structure acts as reflector and providing additional gain and efficiency improvement of 22% and 12%, respectively. The prototype measurement supporting the simulation results with good matching in reflection coefficient and gain. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2401 KB

Open AccessProceeding Paper

Comparison of Transfer Learning Techniques to Classify Brain Tumours Using MRI Images

by Jayneet Jain, Mihika Kubadia, Monika Mangla and Prachi Tawde

Eng. Proc. 2023, 59(1), 144; https://doi.org/10.3390/engproc2023059144 - 4 Jan 2024

Cited by 12 | Viewed by 3630

Abstract

Brain tumour detection and classification are life-saving steps for humanity. There are many medical imaging techniques that can identify abnormal brain diseases. These include nuclear magnetic resonance, ultrasound, X-rays, radionuclides, lasers, electrons, and light. Because of the outstanding image quality and lack of [...] Read more.

Brain tumour detection and classification are life-saving steps for humanity. There are many medical imaging techniques that can identify abnormal brain diseases. These include nuclear magnetic resonance, ultrasound, X-rays, radionuclides, lasers, electrons, and light. Because of the outstanding image quality and lack of ionising radiation, magnetic resonance imaging (MRI) is widely employed in medical imaging. Artificial Intelligence provides an easier way to interpret these MRIs, which is otherwise a tedious and time-consuming task. Deep learning networks and convolutional neural networks have been very good in the detection of brain tumours. In this work, the authors employ deep-learning transfer techniques for the classification of brain tumours. The VGG-16, ResNet-50, and Inception v3 models with CNN pre-training have been utilised by the authors to predict and categorise brain tumours automatically. Using a dataset of 7023 MRI brain tumour images divided into four different classifications, pre-trained models are shown to be effective. The performance of the VGG-16, ResNet-50, and Inception v3 models is compared, and it is established from the experimental evaluation that ResNet-50 outperforms VGG-16 and Inception v3. Thus, the employment of ResNet-50 in tumour classification is validated and advocated. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2766 KB

Open AccessProceeding Paper

Design Implementation of Trapezoidal Notch Band Monopole Antenna for LTE, ISM, Wi-MAX and WLAN Communication Applications

by Gubbala Kishore Babu, Singam Aruna and Kethavathu Srinivasa Naik

Eng. Proc. 2023, 59(1), 145; https://doi.org/10.3390/engproc2023059145 - 5 Jan 2024

Cited by 2 | Viewed by 1406

Abstract

This article analyses & describes a trapezoidal dual-band monopole antenna. The notch band monopole disables 4.4–5.7 GHz commercial communication equipment. The basic type operates at 2.5–4.4 GHz with a 500 MHz marginal bandwidth and 5.7–7 GHz with a 1000 MHz bandwidth. Present research [...] Read more.

This article analyses & describes a trapezoidal dual-band monopole antenna. The notch band monopole disables 4.4–5.7 GHz commercial communication equipment. The basic type operates at 2.5–4.4 GHz with a 500 MHz marginal bandwidth and 5.7–7 GHz with a 1000 MHz bandwidth. Present research optimises multiband trapezoidal antennas. Trapezoidal antennas improve multi-band wireless antennas. GSM, LTE, Wi-Fi, and 5G frequency bands start design. Inefficient and space-wasting, traditional antennas lack frequency range. Benefits of trapezoids: changing trapezoidal element sizes and angles enables the antenna to transmit many frequencies, sloping trapezium sides allow impedance changes without networks or tuning, numerical calculation and electromagnetic modelling optimise the trapezoidal antenna’s performance throughout the communication band, impedance matching, gain, and radiation efficiency provide transmission reliability, and broadband trapezoidal forms eliminate band-specific antennas & switches. Simplified antenna integration makes modern devices cheaper and simpler. In multiband applications, trapezoidal antennas outperform normal antennas. The antenna fits numerous wireless communication devices and systems due to its modest size and wide band coverage. The redesigned structure with notch increases operating band bandwidth and notches application bands between 4.4–5.7 GHz. By modifying trapezoidal geometry, we generate selective impedance transition notches to target crucial interference frequencies. Modern wireless communication systems with complicated interference situations can trust its careful engineering to provide good efficiency and radiation patterns across a wide frequency band while actively rejecting interfering signal. The peak realized gains obtained at 2.5 GHz is 2.4 dB, at 3.4 GHz it is 3.5 GHz and at 5.8 GHz it is 4.7 dB. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 4860 KB

Open AccessProceeding Paper

Identifying Antibiotic-Resistant Mutants in β-Lactamases for Class A and Class B Using Unsupervised Machine Learning

by Soumya Lipsa Rath, Smaranika Mohapatra and Veena Gayathri

Eng. Proc. 2023, 59(1), 146; https://doi.org/10.3390/engproc2023059146 - 5 Jan 2024

Cited by 2 | Viewed by 1831

Abstract

Antimicrobial resistance (AMR) is a significant global concern that endangers human health. To overcome this resistance, β-lactames are used in combination with β -lactamase inhibitors to bypass the enzymatic action. The current study incorporates the techniques of machine learning to cluster the patterns [...] Read more.

Antimicrobial resistance (AMR) is a significant global concern that endangers human health. To overcome this resistance, β-lactames are used in combination with β -lactamase inhibitors to bypass the enzymatic action. The current study incorporates the techniques of machine learning to cluster the patterns of the proteins which may be antibiotic resistant. K-Means Clustering is applied along with PCA analysis, to verify and validate the model’s accuracy where Mean Clustering Analysis was used to validate the number of clusters formed. The result showed 3 clusters in Class A and 4 clusters in Class B representing various characteristics of these mutants. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 3124 KB

Open AccessProceeding Paper

Analytical Modelling of Trapezoidal Monopole Structured Antenna for Wi-Fi, Industrial Scientific and Medical, and Wireless Communication System Applications

by Gubbala Kishore Babu, Singam Aruna and Kethavathu Srinivasa Naik

Eng. Proc. 2023, 59(1), 147; https://doi.org/10.3390/engproc2023059147 - 7 Jan 2024

Cited by 2 | Viewed by 2012

Abstract

A dual-band monopole antenna of a trapezoidal shape is modelled and the analytical study is presented in this article. The designed model is working between 2.5 and 3 GHz by producing bandwidth value of 500 MHz and 4–5 GHz with a bandwidth value [...] Read more.

A dual-band monopole antenna of a trapezoidal shape is modelled and the analytical study is presented in this article. The designed model is working between 2.5 and 3 GHz by producing bandwidth value of 500 MHz and 4–5 GHz with a bandwidth value of 1000 MHz. The designed antenna polarization is linear, and the radiation is non-directive. The performance bandwidth is 4:1, 2:1, and 5:1 at 2.5, 2.6, and 4.5 GHz and the gain value is 2.3 dB, 2.9 dB, and 5.1 dB, respectively. An impedance value of 50 ohms is observed at the port during the analysis. The analysed model is best suitable for the wireless communication applications of ISM, Wi-Fi, and WLAN, with moderate gain and efficiency. In wireless communication systems, effective and adaptable antennas are in high demand. This work proposes an analytical modelling technique for a trapezoidal-structured monopole antenna for Wi-Fi, ISM, and other wireless communication systems. The proposed antenna has a compact size, broad frequency coverage, and omnidirectional radiation patterns. The analytical model considers the antenna’s geometric characteristics, material qualities, and operating frequencies using electromagnetics laws. Through rigorous mathematical definitions, the study reveals the antenna’s resistance, radiation efficiency, and gain patterns across the necessary frequency bands. Furthermore, this analytical model predicts antenna performance without time-consuming simulation or costly prototypes. A thorough analysis assesses the trapezoidal monopole antenna’s suitability for Wi-Fi, ISM, and wireless communication applications, addressing their individual requirements and limit. The bandwidth, gain, radiation efficiency, and impedance matching are examined to show the antenna’s capacity to fulfil modern wireless system needs. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 3104 KB

Open AccessProceeding Paper

Nonlinear Behavior of Cold-Formed Steel Columns: Investigating the Influence of Stiffener on Strength and Buckling Resistance

by Premalatha Palanivelu, Kalpana Kaliyamoorthi, Abdul Rahman Jabarullah and Rubijayabakkiamani Samymuthu

Eng. Proc. 2023, 59(1), 148; https://doi.org/10.3390/engproc2023059148 - 9 Jan 2024

Viewed by 2168

Abstract

Steel structures are widely employed in the construction industry because of their simplicity, speed of construction, and ease of handling. Cold-formed steel is becoming more popular in the construction industry as the sections are created using thin-gauge sheets, as a result of which [...] Read more.

Steel structures are widely employed in the construction industry because of their simplicity, speed of construction, and ease of handling. Cold-formed steel is becoming more popular in the construction industry as the sections are created using thin-gauge sheets, as a result of which the weight of the structure is reduced. This saves a lot of steel compared to normal steel structures, providing cost benefits and material savings. Finding a cross-section that is both cost-effective and able to carry more weight without buckling presents a challenge. The objective of this investigation was to analyze the effects of a stiffener on the behavior of cold-formed steel columns. An experimental study was carried out on two long columns made of cold-formed steel with back-to-back lipped channel sections—one with stiffener and the other without stiffener. A finite element model was developed and validated using the experimental and theoretical results. The theoretical investigation was based on the direct strength method and effective width method using IS codes. From the results, it was observed that intermediate V-shaped web stiffeners improved the distortional and local buckling strength. A non-linear behavior of the stress–strain curve was observed. The applied stiffener did not increase the dimensions or required material of the section, but the results predicted an increase in strength of 32%. This model could be further utilized for various parametric studies and more effective sections could be achieved. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 3304 KB

Open AccessProceeding Paper

A Compact CPW-Fed Textile-Substrate-Based Half-Circula Spike Monopole Antenna

by Rajesh Katragadda and Palasetti Appala Nageswara Rao

Eng. Proc. 2023, 59(1), 149; https://doi.org/10.3390/engproc2023059149 - 9 Jan 2024

Cited by 1 | Viewed by 1635

Abstract

A coplanar-waveguide-type fed half-circular spike-shaped monopole antenna is designed on textile substrates and analyzed in this paper. The most suitable textile substrate is identified in this work by testing the current model performance characteristics on silk, jeans and cotton fabrics and is presented [...] Read more.

A coplanar-waveguide-type fed half-circular spike-shaped monopole antenna is designed on textile substrates and analyzed in this paper. The most suitable textile substrate is identified in this work by testing the current model performance characteristics on silk, jeans and cotton fabrics and is presented this analytical study. The cotton material model provided a bandwidth of 9.4 GHz, the silk material provided a 9.2 GHz bandwidth and the jeans material provided 9.1 GHz. A maximum gain of 9.5 dB was attained for 3.6 GHz of the 5G band and 8.2 dB for 5.8 GHz of the WLAN band. The antenna is prototyped on cotton substrate, bending analysis is also performed at 15 degrees, 30 degrees and 45 degrees in vertical and horizontal conditions and we find satisfactory results for the specified application. Compact, wearable antennas with varied performance are in demand as wireless communication systems evolve. The antenna is designed for wearable and textile-integrated wireless communication. The textile substrate makes the antenna flexible and can be integrated into garments, wearable gadgets and smart textiles. This paper describes how to choose textile materials and design a half-circular spike monopole antenna. Electromagnetic simulations evaluate the antenna’s impedance matching, radiation pattern and bandwidth. The CPW feedline is designed to efficiently transfer power to the antenna, improving performance. This study also examines the antenna’s longevity and resilience in textile materials, addressing real-world issues like bending and washing. This examination verifies the antenna’s wearable functionality and reliability. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1565 KB

Open AccessProceeding Paper

An Internet of Things-Enabled Self-Stabilizing Spoon for Patients with Parkinson’s Disease

by Chirag Chaturvedi, Vishal Vinod Hingorani and Abhishek Gudipalli

Eng. Proc. 2023, 59(1), 150; https://doi.org/10.3390/engproc2023059150 - 9 Jan 2024

Cited by 1 | Viewed by 9997

Abstract

The second most frequent type of neurodegenerative sickness is Parkinson’s disease, which impairs daily functions and movement in older people due to significant nerve cell destruction. The patient may experience uncontrollable shaking and hand tremors as their condition worsens, making it difficult for [...] Read more.

The second most frequent type of neurodegenerative sickness is Parkinson’s disease, which impairs daily functions and movement in older people due to significant nerve cell destruction. The patient may experience uncontrollable shaking and hand tremors as their condition worsens, making it difficult for them to carry out routine chores, such as eating from a bowl. In this project, we want to build a stabilising spoon for individuals with Parkinson’s disease by using the concepts of sensor networks and the Internet of Things. The stabilising spoon senses any inadvertent tremors or shivers from the user and modifies its head appropriately, ensuring that the spoon’s bowl stays stable at all times. A prototype was developed using an accelerometer to monitor motion speed, as well as a gyroscope to estimate angle in order to assist patients throughout the eating process. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 3771 KB

Open AccessProceeding Paper

Development of Gas Dynamic Nozzles: A Preliminary Computational Study

by Prasad Savanur, Ganapuram Venu, G. N. Kumar, M. V. Ramaprasad and Padmavati K. Uttarwar

Eng. Proc. 2023, 59(1), 151; https://doi.org/10.3390/engproc2023059151 - 9 Jan 2024

Viewed by 1878

Abstract

The design and optimization of supersonic nozzles are of great interest in aerospace and propulsion system applications. Designing and maintaining a mechanically simple nozzle would be beneficial over the complex nozzles that are currently in use. A detailed simulation-based study was conducted to [...] Read more.

The design and optimization of supersonic nozzles are of great interest in aerospace and propulsion system applications. Designing and maintaining a mechanically simple nozzle would be beneficial over the complex nozzles that are currently in use. A detailed simulation-based study was conducted to design a virtual nozzle that produces the same effect as a traditional nozzle while using simpler geometries and secondary air injection. Secondary air injection is widely used for fluidic thrust vectoring. Because such a nozzle is operated by varying the thickness of the boundary layer, it is possible to control the effective throat area, and hence, achieve a variety of exit pressures and Mach numbers by just varying the input momentum ratios (or pressure ratios). A similar concept was kept in mind, and various geometries, such as flat-plate geometry, divergent geometry and convergent–divergent geometry, were tested for different jet pressure ratios, numbers of jets, locations of the jets and geometric parameters of the virtual nozzle. The main objective of the work was to achieve an exit Mach number of 2 from a subsonic flow. The lengths of various geometries and the input pressure ratios were altered iteratively based on the findings obtained in each test case. All of the results attempted to acquire the required exit Mach number while accounting for numerous complexities in fluid flows, such as shock waves, vorticities, etc. Although the desired Mach number is not achieved, this study establishes a strong foundation and an idea that has the potential to revolutionize propulsion systems and create nozzles that are mechanically simple, weigh less and do not require any actuating mechanisms to operate. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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13 pages, 1785 KB

Open AccessProceeding Paper

Development of a Modified Medical Data Transmission over the Cellular Network System to Secure Health-Related Data from Changes in Environmental Parameters

by Krishnaveni Kommuri and Venkata Ratnam Kolluru

Eng. Proc. 2023, 59(1), 153; https://doi.org/10.3390/engproc2023059153 - 12 Jan 2024

Cited by 2 | Viewed by 1876

Abstract

Patients are generally sent to hospitals during emergencies and life-threatening conditions using ambulances. The health problems of patients become more serious when the treatment is delayed. If the vital signs of patients inside an ambulance or a treatment area sent to a hospital [...] Read more.

Patients are generally sent to hospitals during emergencies and life-threatening conditions using ambulances. The health problems of patients become more serious when the treatment is delayed. If the vital signs of patients inside an ambulance or a treatment area sent to a hospital in real time, the odds of saving lives will improve considerably. The patient’s medical needs can be arranged by paramedics with the doctors’ instructions until their arrival at the hospital. Information from past vital signs can also be archived their medical history. The Internet of Things (IoT) is a paradigm that visualizes practically everything connected to the Internet. This opens access to a lot of tiny medical needs and emergency relief tools. As a proof of concept, a test model prototype was implemented using an IoT-enabled ambulatory vital sign sensor board and a remote hospital framework. The objective of the implementation of such a prototype blends IoT technology with healthcare services to provide a more efficient and patient-centred approach to monitoring and controlling health issues, particularly in instances when continuous remote monitoring is advantageous. The working of the proposed device was validated and the results were monitored for the health-related data collected during the testing period. This strategy promotes health monitoring in emergencies with eHealth Signals for medical assistance. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 661 KB

Open AccessProceeding Paper

A Review on Graphitic Carbon Nitride and Conducting Polymer Nanocomposite Electrodes for Supercapacitors

by Priyanka Chaluvachar, Gonuru Thammanaiah Mahesha, Yethadka Narahari Sudhakar, Vishnu Nair and Dayananda Pai

Eng. Proc. 2023, 59(1), 154; https://doi.org/10.3390/engproc2023059154 - 12 Jan 2024

Cited by 14 | Viewed by 3455

Abstract

The growing demands of next-generation electric and hybrid electric vehicles and high-power electronic devices necessitate higher power density, longer cycle life, and enhanced safety at a reduced cost. To address these challenges, supercapacitors have emerged as a potential technology offering several advantages such [...] Read more.

The growing demands of next-generation electric and hybrid electric vehicles and high-power electronic devices necessitate higher power density, longer cycle life, and enhanced safety at a reduced cost. To address these challenges, supercapacitors have emerged as a potential technology offering several advantages such as higher power density, excellent cycle stability, environmental friendliness, and wide temperature-range performance. Recently, research has focused on developing nanomaterials that would improve the capacitive performance of supercapacitors. Graphitic carbon nitride (g-CN or g-C₃N₄) exhibits distinct chemical and physical characteristics that are advantageous for diverse applications including energy conversion and storage. g-CN integrates the benefits of nitrogen doping, such as increased surface polarity and better surface wettability, with the advantages of carbon compounds, such as ease of availability, abundance in nature, and cost efficiency. The considerable advance in research on g-CN has inspired the development of various g-CN nanocomposites to achieve high efficiency by eliminating certain limitations. To overcome the issues related to conductivity and specific surface area, g-CN can be composited with conducting polymers (CP) as one of the modification strategies. Recently researchers have experimented with various g-CN-conducting polymer nanocomposites as electrode materials for supercapacitors. Based on the studies conducted, g-CN-conducting polymer nanocomposites have achieved good stability, adequate conductivity, and better specific capacitance. This review provides an overview of g-CN/conducting polymer nanocomposites as supercapacitor electrode materials. It covers synthetic strategies, discusses factors affecting their electrochemical performance, and outlines future research directions for high-performance supercapacitors. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2556 KB

Open AccessProceeding Paper

Phase-Image-Encryption-Based Elliptic Curve and Double-Random-Phase Encoding

by Arabind Kumar, Sanjay Yadav and Tarul Garg

Eng. Proc. 2023, 59(1), 155; https://doi.org/10.3390/engproc2023059155 - 11 Jan 2024

Cited by 1 | Viewed by 1572

Abstract

In this paper, we proposed an enhanced asymmetric cryptosystem scheme for image encryption using a combination of Elliptic Curve and Fourier transformations. Our proposed encryption and decryption process is highly secure with a smaller key size compared to other schemes due to the [...] Read more.

In this paper, we proposed an enhanced asymmetric cryptosystem scheme for image encryption using a combination of Elliptic Curve and Fourier transformations. Our proposed encryption and decryption process is highly secure with a smaller key size compared to other schemes due to the use of Elliptic Curve Cryptography. The experimental results prove that the image-encryption scheme proposed in this research is effective and has strong anti-attack and key sensitivity. Computer-based simulations have been performed for this scheme to complete the measurable examination utilizing histograms plots, and correlation distribution of adjacent pixels. Moreover, the security of this encryption scheme relies on Elliptic Curve Cryptography, which has high security. The validation of the scheme is shown using a grayscale image and all the computations are performed in MATLAB (R2021a). The security against several attacks like noise is also shown. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 2730 KB

Open AccessProceeding Paper

Assessing the Friction and Wear Behavior of AZ91-Based Hybrid Composites Reinforced with Nano hBN/Micron TiB₂ Ceramic Particles Using WASPAS and ARAS Techniques

by Hariharasakthisudhan Ponnarengan, Sathish Kannan and Logesh Kamaraj

Eng. Proc. 2023, 59(1), 156; https://doi.org/10.3390/engproc2023059156 - 12 Jan 2024

Cited by 1 | Viewed by 1509

Abstract

The combination of the lightweight nature and mechanical properties of AZ91 makes it a suitable material for defense, aerospace, and automotive components. The study of the friction and wear properties of AZ91 contributes to the understanding of interactions of surfaces in relative motion. [...] Read more.

The combination of the lightweight nature and mechanical properties of AZ91 makes it a suitable material for defense, aerospace, and automotive components. The study of the friction and wear properties of AZ91 contributes to the understanding of interactions of surfaces in relative motion. Hybrid ceramic reinforced composites can be tailored to offer enhanced mechanical and tribological properties. The present study highlights the development of AZ91-based hybrid composites reinforced with nano hBN and micron-sized TiB₂ ceramic particles. The hBN is used as a hybridizing agent in the perspective of improving the friction and wear behavior of the composites. The Taguchi L16 orthogonal array was used to prepare the experimental plan. The normal load, sliding speed, and sliding distance were considered as influencing factors in the experiments against the responses, wear rate, and coefficient of friction. Multi-Criteria Decision-Making methods such as Additive Ratio Assessment System (ARAS) and Weighted Aggregated Sum Product Assessment (WASPAS) were employed to optimize the experiments. The presence of hBN decreased the wear rate and coefficient of friction of the hybrid composites. The adhesive mode of wear mechanism was found to be operative in the composites. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 5689 KB

Open AccessProceeding Paper

Analysis of Mechanical Properties of Casted Aluminium Alloy for Automotive Safety Application

by Sourav, Somanagouda Patil, Naveen Chandra, Nithin Kumar, Dilip Kumar and Rashmi P. Shetty

Eng. Proc. 2023, 59(1), 157; https://doi.org/10.3390/engproc2023059157 - 12 Jan 2024

Cited by 2 | Viewed by 1919

Abstract

Automotive safety encompasses various measures, including seat belts, airbags, and advanced driver assistance systems, to minimise the risk of accidents and protect vehicle occupants. Seat belts play a crucial role in restraining occupants during collisions, reducing the likelihood of serious injuries. A part [...] Read more.

Automotive safety encompasses various measures, including seat belts, airbags, and advanced driver assistance systems, to minimise the risk of accidents and protect vehicle occupants. Seat belts play a crucial role in restraining occupants during collisions, reducing the likelihood of serious injuries. A part of a vehicle’s seat belt system is commonly referred to as a “retractor spindle”. The seat belt webbing’s movement and tension are managed by the seat belt retractor spindle. The selection of spindle material is crucial for seat belt retraction and extraction, with aluminium alloy being favoured due to its light weight and high strength, ensuring efficient and reliable performance in automotive safety systems. In this regard, an attempt was made to create a simulation material model for

{A l S i}_{9} {C u}_{3}

, which in turn led to a spindle break load simulation. For a specimen and a spindle made of the same material, experimental and finite element analyses were conducted. Specimen-level tests were carried out, and behaviour was studied using the MAT_ADD_EROSION damage model and the MAT_PLASTICITY_COMPRESSION_TENSION material model in LS-Dyna. The obtained ultimate strain value was used create the material card. Spindle analysis was carried out with the same control cards and material cards. From the experimental tests and finite element analysis, we conclude that the proposed simulation material model for

{A l S i}_{9} {C u}_{3}

predicts the spindle breaking load and failure modes to acceptable levels. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 5710 KB

Open AccessProceeding Paper

Resolution Enhancement of Brain MRI Images Using Deep Learning

by Minakshi Roy, Biraj Upadhyaya, Jyoti Rai and Kalpana Sharma

Eng. Proc. 2023, 59(1), 158; https://doi.org/10.3390/engproc2023059158 - 15 Jan 2024

Cited by 4 | Viewed by 3326

Abstract

One of the most widely used imaging techniques in medicine is magnetic resonance imaging (MRI). It is a tool that doctors use to comprehend human anatomy and carry out more accurate analyses. In the study of brain anatomy, image processing super resolution technology [...] Read more.

One of the most widely used imaging techniques in medicine is magnetic resonance imaging (MRI). It is a tool that doctors use to comprehend human anatomy and carry out more accurate analyses. In the study of brain anatomy, image processing super resolution technology has become important to overcome physical restrictions due to image deterioration caused by hardware constraints, lengthier scanning periods, and artefacts. Super resolution is an approach to raise an image’s resolution while improving the image’s quality from a low-resolution (LR) image to a higher-resolution (HR) image. The study provides an overview of deep learning techniques for creating super-resolution (SR) MRI brain images. A widely used deep learning (DL) technique, accessible brain MRI dataset, and quantity evaluation matrices have been presented, mostly used for image super resolution. Factors affecting hardware constraints and artifacts, including magnetic field homogeneity, gradient nonlinearity, radiofrequency (RF) coil sensitivity, signal-to-noise ratio (SNR), and gradient coil performance, have been taken into account. This research focuses mostly on brain MRI images as a contribution to the medical industry for super resolution. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1092 KB

Open AccessProceeding Paper

Green Hydrogen as a Clean Energy Resource and Its Applications as an Engine Fuel

by Sumit Taneja, Ankur Jain and Yash Bhadoriya

Eng. Proc. 2023, 59(1), 159; https://doi.org/10.3390/engproc2023059159 - 15 Jan 2024

Cited by 16 | Viewed by 4841

Abstract

The world’s economy heavily depends on the energy resources used by various countries. India is one of the promising developing nations with very low crude reserves actively looking for new renewable energy resources to power its economy. Higher energy consumption and environmental pollution [...] Read more.

The world’s economy heavily depends on the energy resources used by various countries. India is one of the promising developing nations with very low crude reserves actively looking for new renewable energy resources to power its economy. Higher energy consumption and environmental pollution are two big global challenges for our sustainable development. The world is currently facing a dual problem of an energy crisis as well as environmental degradation. So, there is a strong need to reduce our dependency on fossil fuels and greenhouse gas emissions. This can be achieved to a great extent by universally adopting clean fuels for all daily life uses, like ethanol or liquified natural gas (LNG), as these burn very clean and do not emit many pollutants. Nowadays, green hydrogen has emerged as a new clean energy source, which is abundantly available and does not pollute much. This article explores the various benefits of green hydrogen with respect to fossil fuels, various techniques of producing it, and its possible use in different sectors such as industry, transport, and aviation, as well as in day-to-day life. Finally, it explores the use of green hydrogen as fuel in automobile engines, its blending with CNG gas, and its benefits in reducing emissions compared to fossil fuels. On combustion, green hydrogen produces only water vapours and is thus a highly clean fuel. Thus, it can potentially help humanity preserve the environment due to its ultra-low emissions and can be a consistent and reliable source of energy for generations to come, thereby ending the clean energy security debate forever. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 873 KB

Open AccessProceeding Paper

Biocompatibility and Performance of Dental Composite Restorations: A Narrative Review on Free Monomer Release, Concerns and Solutions

by Aastha Dureja, Shashi Rashmi Acharya, Sandya Kini, Arun Mayya and Veena Hedge

Eng. Proc. 2023, 59(1), 160; https://doi.org/10.3390/engproc2023059160 - 15 Jan 2024

Cited by 11 | Viewed by 9080

Abstract

The use of resin-based dental composites is multiplying through the years due to the increased demand for tooth-colored restorations. The choice of monomers strongly determines the viscosity, reactivity, mechanical property, water sorption and polymerization shrinkage of the composite material. It is desirable for [...] Read more.

The use of resin-based dental composites is multiplying through the years due to the increased demand for tooth-colored restorations. The choice of monomers strongly determines the viscosity, reactivity, mechanical property, water sorption and polymerization shrinkage of the composite material. It is desirable for all monomers to be converted into polymers (Degree of Conversion), but this does not occur clinically, resulting in a poor prognosis for restorations as well as an increase in systemic health risks. The release of monomers occurs due to erosion and degradation, as well as the release of leachable species from the restoration. The potential toxicity of free monomers to dental pulp cells is concerning. Free monomers are not only allergens but also have reported cytotoxic and genotoxic effects. Various methods and practices have thus been employed to counter the ill effects of free monomer release from dental composite restorations for better safety and healthy oral cavities. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2178 KB

Open AccessProceeding Paper

Modern Communication Methods in Higher Education: A Post-COVID-19 Analysis

by Bharti, Abhishika Sharma and Anand Pandey

Eng. Proc. 2023, 59(1), 161; https://doi.org/10.3390/engproc2023059161 - 15 Jan 2024

Cited by 4 | Viewed by 4131

Abstract

During COVID-19, the traditional educational landscape witnessed the rapid and unprecedented adoption of modern communication methods to facilitate remote learning and academic interactions. Various online education platforms have enriched content deliveries, paved the way for learners across the globe, and enriched the learning [...] Read more.

During COVID-19, the traditional educational landscape witnessed the rapid and unprecedented adoption of modern communication methods to facilitate remote learning and academic interactions. Various online education platforms have enriched content deliveries, paved the way for learners across the globe, and enriched the learning environment for learners and facilitators who design, deliver, and try their best to make it exciting and engaging. Apart from this, even the traditional mode of education encourages the use of blended and hybrid learning so that deliverables are improved. Advancement in the usage of hybrid, innovative smart classes is encouraged by higher educational institutions. This study delves into the paradigm shift in higher education brought about by the COVID-19 pandemic, explicitly focusing on the transformation of communication methods. It also focuses on the various effective communication methods (online and physical classes) that higher education institutions adopt. This study considers secondary data and argues on online learning skills, classroom learning/flipped classroom method, problem-based learning, cooperative learning, assessment evaluation techniques, the four-quadrant approach, and outcome-based teaching–learning pedagogy for all higher education programs. Furthermore, the research considers the long-term impact of these modern communication methods on the future of higher education. It explores whether adopting these technologies will persist or evolve as institutions transition back to in-person learning and whether a blended approach to education will emerge. In conclusion, this research provides a timely assessment of the transformation of communication methods in higher education post-COVID-19, shedding light on the opportunities, challenges, and potential pathways for the sustainable integration of modern communication methods in the academic realm. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 845 KB

Open AccessProceeding Paper

Navigating the Divide: Digital Kiosks and Mobile Apps as Complementary Human-Centered Self-Service Technologies

by Amani S. Aljohi, Sara S. Alzaabi, Rahma S. Almahri, Georgios Tsaramirsis and Oussama H. Hamid

Eng. Proc. 2023, 59(1), 162; https://doi.org/10.3390/engproc2023059162 - 15 Jan 2024

Cited by 2 | Viewed by 3938

Abstract

This work sheds light on the effectiveness of digital kiosks in targeting specific audiences in contrast to centrally managed mobile phone applications. To this end, we have conducted a case study where a digital kiosk was developed to support the academic activities of [...] Read more.

This work sheds light on the effectiveness of digital kiosks in targeting specific audiences in contrast to centrally managed mobile phone applications. To this end, we have conducted a case study where a digital kiosk was developed to support the academic activities of the computer science department. Our results show that the students continue to use the mobile phone application. However, the digital kiosk added the following main benefits to the service: Firstly, being in a physical location and thanks to their larger screens, digital kiosks are ‘eye-catching’ devices, which makes them ideal for advertising products/services or communicating relevant information. Secondly, they are brilliant points of attraction. By seeing other people standing in front of any of them, members of the target audience are encouraged to imitate them, even if they did not have the intention to do so. Thirdly, even if the services are available from a mobile phone application, some people do not wish to create an account, download and install the application on their devices, and/or give permission to it, which can potentially invade their privacy and security. Lastly, and equally important, digital kiosks are human-centered technologies that can be more appealing to people who seek social interactions. With this, we conclude that digital kiosks cannot replace mobile phone applications. Rather, they are further technologies that enhance self-service overall. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 3084 KB

Open AccessProceeding Paper

An Analytical Model for Dynamic Spectrum Sensing in Cognitive Radio Networks Using Blockchain Management

by Nikhil Kumar Marriwala, Sunita Panda, Chandran Kamalanathan, Narayanan Sadhasivam and Vootla Subba Ramaiah

Eng. Proc. 2023, 59(1), 163; https://doi.org/10.3390/engproc2023059163 - 15 Jan 2024

Cited by 10 | Viewed by 2205

Abstract

Recent advancements in wireless communication technology have brought about the pressing issue of increasing spectrum scarcity. This challenge in spectrum allocation arises from ongoing research in the field of wireless communication. Unfortunately, a significant portion of the spectrum remains underutilized within wireless networks. [...] Read more.

Recent advancements in wireless communication technology have brought about the pressing issue of increasing spectrum scarcity. This challenge in spectrum allocation arises from ongoing research in the field of wireless communication. Unfortunately, a significant portion of the spectrum remains underutilized within wireless networks. Cognitive radio (CR) presents an innovative solution to this problem by enabling unlicensed secondary users to coexist with licensed primary users within allocated spectrum bands without causing interference to the primary users’ communications. This paper promises to address the spectrum redundancy challenges and substantially improve the spectrum utilization efficiency. Cognitive radio networks (CRNs), alternatively known as dynamic spectrum access networks, are comprised of multiple CR nodes and are frequently referred to as next generation (XG) communication networks. These XG communication networks are expected to offer high-speed data transmission capabilities to adaptable users through a variety of wireless architectures and dynamic access protocols. Since CRNs share similarities with traditional wireless networks but operate in an external wireless medium, they are more susceptible to various types of attacks compared to their wired counterparts. This vulnerability stems from the fact that wireless media can be intercepted or exploited, potentially leading to channel congestion or data interception. This paper presents two key approaches: the node evaluation and selection (NES) algorithm and the secure spectrum sensing mechanism, which incorporate the user’s interaction history and connection distance, that are recorded in a public ledger and managed by a blockchain management system. The proposed algorithm facilitates the central aggregation point for selecting nodes with outstanding performance for cooperative sensing, thus enhancing the network’s security against malicious node attacks. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 514 KB

Open AccessProceeding Paper

Spectrophotometric Method for the Determination of Ciprofloxacin in Pure and Pharmaceutical Preparations: Development and Validation

by Tariq Yassin Mahmoud, Isam Shaker Hamza and Aziz Latif Jarallah

Eng. Proc. 2023, 59(1), 164; https://doi.org/10.3390/engproc2023059164 - 16 Jan 2024

Cited by 6 | Viewed by 10493

Abstract

Ciprofloxacin (Cip) is spectrophotometrically identified through the formation of a colored charge-transfer complex that exhibits a maximum absorbance at 440 nm. This complex is generated by the reaction of the drug’s secondary amine with sodium nitroprusside (SNP) in an alkaline medium in the [...] Read more.

Ciprofloxacin (Cip) is spectrophotometrically identified through the formation of a colored charge-transfer complex that exhibits a maximum absorbance at 440 nm. This complex is generated by the reaction of the drug’s secondary amine with sodium nitroprusside (SNP) in an alkaline medium in the presence of hydroxylamine (NH₂OH). Classical univariate analysis is employed to optimize the experimental conditions affecting the formation of the charge-transfer (CT) complex. The method presented herein offers a straightforward and sensitive approach for quantifying ciprofloxacin within a concentration range of 50.0–250.0 μg/mL. The method exhibits a molar absorptivity of 364.4817 L/mol·cm and a coefficient of determination (

r^{2}

) of 0.997. Validation of the method is achieved through determination of the regression equation, accuracy, precision, and detection limit. The procedure is successfully applied to the quantification of ciprofloxacin in pharmaceutical formulations and demonstrates satisfactory recovery and precision. Statistical validation corroborates the reliability and repeatability of the obtained results. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 2727 KB

Open AccessProceeding Paper

Blockchain-Based Network Optimization for Workstation Nodes

by Pankaj Kunekar, Shubham Mulay, Dnyaneshwari Navale, Akhilesh Nawale, Vishal Sonkusale and Vishwam Talnikar

Eng. Proc. 2023, 59(1), 165; https://doi.org/10.3390/engproc2023059165 - 17 Jan 2024

Cited by 1 | Viewed by 2232

Abstract

Computer networks are used for internet access, cloud computing, and telecommunication. Network optimization is the process of increasing the speed and efficiency of the communication process between nodes on a network. The concept utilizes blockchain as a tool. Network optimization is an important [...] Read more.

Computer networks are used for internet access, cloud computing, and telecommunication. Network optimization is the process of increasing the speed and efficiency of the communication process between nodes on a network. The concept utilizes blockchain as a tool. Network optimization is an important and challenging problem in network design and routing. The goals of network optimization are to decrease the number of hops while maintaining the quality of service guarantees and to minimize the amount of energy used in communications. Traditional network architectures rely on centralized servers or data centers, introducing potential bottlenecks and single points of failure. In contrast, blockchain offers a decentralized approach, enabling nodes to communicate directly without dependence on a central authority. Its unique features include its ability to provide transaction transparency and immutable record keeping. In this paper, we study an efficient system to demonstrate real-time traffic and understand the fundamentals of networking. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 968 KB

Open AccessProceeding Paper

Comparison of Different Machine Learning Algorithms to Classify Epilepsy Seizure from EEG Signals

by Pankaj Kunekar, Chanchal Kumawat, Vaishnavi Lande, Sushant Lokhande, Ram Mandhana and Malhar Kshirsagar

Eng. Proc. 2023, 59(1), 166; https://doi.org/10.3390/engproc2023059166 - 16 Jan 2024

Cited by 9 | Viewed by 2400

Abstract

Recurrent seizures are a symptom of a central nervous system disease called epilepsy. The duration of these seizures lasts less than a few seconds or sometimes minutes. There are very few ways to record seizures, and one of them is EEG. EEG systems [...] Read more.

Recurrent seizures are a symptom of a central nervous system disease called epilepsy. The duration of these seizures lasts less than a few seconds or sometimes minutes. There are very few ways to record seizures, and one of them is EEG. EEG systems mainly consist of scalp electrodes that record electrical activity. These EEG data are often complex signals containing noise and artifacts. Accurate classification of epileptic seizures is a major challenge, as manual seizure identification is a laborious and challenging endeavor for neurologists. An automated method for seizure detection and categorization was required to address this issue. In this paper, we used machine learning and proposed a model that predicts the behavior of these signals and classifies seizures. The Epileptic Seizure Recognition Data Set from the UCI Machine Learning Repository was the dataset used in this work. The model is evaluated on various models such as XGboost, Extra Tree Classifier, Random Forest, etc. Using measures like F1 score, recall, and precision, the proposed approaches have been assessed. The results indicate that Random Forest produced the superior result of 0.943 F1 score, and XGB achieved a slightly lower F1 score of 0.933. Moreover, Random Forest has the highest accuracy of 0.977. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1126 KB

Open AccessProceeding Paper

A Review of Recent Developments in 6G Communications Systems

by Srikanth Kamath, Somilya Anand, Suyash Buchke and Kaushikee Agnihotri

Eng. Proc. 2023, 59(1), 167; https://doi.org/10.3390/engproc2023059167 - 17 Jan 2024

Cited by 15 | Viewed by 6884

Abstract

Currently, we exist in the 5G division of the wireless technology cycle, where the standardization is complete and deployment is being carried out. However, 5G networks do not have the capacity to deliver an automated and intelligent network that supports connected intelligence. 6G [...] Read more.

Currently, we exist in the 5G division of the wireless technology cycle, where the standardization is complete and deployment is being carried out. However, 5G networks do not have the capacity to deliver an automated and intelligent network that supports connected intelligence. 6G is what enables this, and globally, countries are aiming to lay the foundation for the communication needs of 2030. This brings out a very key question and discussion on how wireless communications will develop in the future, particularly adapting to the range and set of applications and user cases. Industry and academic efforts have started to explore beyond 5G and uncover 6G as 5G becomes more internationally accessible. We forecast that 6G will undergo a transition that is unheard of in the history of wireless cellular systems. 6G exists beyond mobile internet and will be required to support omnipresent AI services from the network’s core to its endpoints. Meanwhile, artificial intelligence (AI) will be crucial for developing and improving 6G designs, protocols, and operations. URLLC plays a crucial role in next-generation communication systems, particularly in 6G, for applications requiring ultra-low latency and reliability. These services support cutting-edge technologies like driverless vehicles, remote robotic surgery, smart factories, and augmented reality applications. URLLCs ensure robust connectivity and real-time responsiveness, enabling time-sensitive and safety-critical services in 6G communication infrastructures. This article illustrates the importance of URLLCs in 6G and their integration with deep learning, the security challenges, and their potential solutions. Further on, it establishes its relationship with key aspects of federated learning and security in the 6G domain. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 2709 KB

Open AccessProceeding Paper

Investigation on the Acoustic Performance of Micro-Perforated Panel Integrated Coiled-Up Space Acoustic Absorber

by Damodaran Sanalkumar Govind Krishna, Parvathy Arun Leena, Abhinav Karottuthundathil, Ashidha Mohammed, Mahesh Kavungal and Mini Rema Sahadevan

Eng. Proc. 2023, 59(1), 168; https://doi.org/10.3390/engproc2023059168 - 17 Jan 2024

Cited by 1 | Viewed by 2821

Abstract

Recently, increased attention has been given to minimize the effects of noise pollution on living beings. The attenuation and manipulation of sound waves with low-frequency components are quite difficult with traditional absorbers due to inherent properties induced by large wavelengths and yet are [...] Read more.

Recently, increased attention has been given to minimize the effects of noise pollution on living beings. The attenuation and manipulation of sound waves with low-frequency components are quite difficult with traditional absorbers due to inherent properties induced by large wavelengths and yet are particularly critical to modern designs. In this study, a parallel arrangement of a coiled-up space cavity and micro-perforated panel (MPP) is considered as the absorber configuration. The coiled-up space consists of a front panel with an orifice and a rigid backing panel enclosing an arch-shaped concentric channel. The entire coiled-up space length is provided with two varying cross-sections. By this arrangement, the sound path is squeezed into a reasonably small volume enabling sound absorption at low frequencies. A thin panel with numerous perforations is the main constituent of MPP. It is backed by an air cavity and terminated by a rigid backing. Here in this configuration, micro perforations are provided on the front panel of the coiled-up space, which ensures simultaneous entry of acoustic waves into the micro-perforations and coiled-up space structure. The absorption characteristics of the present configuration are studied numerically and analytically. The combined structure with parallel combination of coiled-up space and MPP resulted in the abatement of more than 70% of sound in the frequency range of 321 Hz to 853 Hz. The present absorber has only a 5.5 cm thickness, which is subwavelength

(\frac{λ}{19})

also. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 361 KB

Open AccessProceeding Paper

A Study on the Suitability of Constant Boundary Elements for the Simulation of Biological Organs

by Kirana Kumara P

Eng. Proc. 2023, 59(1), 169; https://doi.org/10.3390/engproc2023059169 - 17 Jan 2024

Viewed by 978

Abstract

In the process of designing of surgical simulators, it may be a requirement to simulate biological organs in real-time. About thirty computations per second are required for achieving real-time graphics. Hence, computational techniques employed to simulate biological organs in real-time should be able [...] Read more.

In the process of designing of surgical simulators, it may be a requirement to simulate biological organs in real-time. About thirty computations per second are required for achieving real-time graphics. Hence, computational techniques employed to simulate biological organs in real-time should be able to perform about thirty computations per second. The computational techniques employed should be fast enough, but at the same time should be accurate enough to realistically simulate the biological organs which are inherently nonlinear. A numerical technique called the Boundary Element Method (BEM) is generally thought of as being faster when one compares the technique with some well-established numerical techniques like the Finite Element Method (FEM). This technique (BEM) is even faster if constant boundary elements are employed. However, the BEM is mostly used to simulate linear behavior, whereas the FEM is more established for simulating nonlinear behavior. The present work investigates whether biological organs may be simulated by using the linear BEM. The reason nonlinear BEM has not been used is that the nonlinear BEM is quite slow and difficult to implement. A human kidney is the biological organ considered in this work. A nonlinear analysis and a linear analysis are carried out on the kidney. A nonlinear analysis is carried out by using the FEM, whereas the linear analysis is carried out by using the BEM. Results from the nonlinear analysis are compared with the results from the linear analysis. The results indicate that there is good agreement between the results from the linear BEM and the nonlinear FEM many times, but there is considerable difference between the results other times. Although the results reinforce the idea that the BEM could be a useful tool while simulating biological organs, further research is needed to definitively say whether the results given by the linear BEM, which uses constant boundary elements, are always good enough for simulating biological organs. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 5979 KB

Open AccessProceeding Paper

Comparative Evaluation of the Antimicrobial Efficacy of Sodium Hypochlorite, Silver Nanoparticles, and Zinc Nanoparticles against Candidal Biofilm: An In Vitro Study

by Varsha Ravi, Sandya Kini, Neetha Shenoy, Krishnaraj Somayaji and Padmaja Shenoy

Eng. Proc. 2023, 59(1), 170; https://doi.org/10.3390/engproc2023059170 - 17 Jan 2024

Cited by 3 | Viewed by 3039

Abstract

The aim of the present investigation was to evaluate and compare the antifungal activity of sodium hypochlorite (NaOCl), silver nanoparticles (Ag-NP), and zinc nanoparticles (ZnO-NP) against candidal biofilms. Twenty-two single-rooted premolars were decoronated to a root length of 12 mm. Shaping of the [...] Read more.

The aim of the present investigation was to evaluate and compare the antifungal activity of sodium hypochlorite (NaOCl), silver nanoparticles (Ag-NP), and zinc nanoparticles (ZnO-NP) against candidal biofilms. Twenty-two single-rooted premolars were decoronated to a root length of 12 mm. Shaping of the canals was done with ProTaper size F2 files. The specimens were inserted into vials containing 2 mL of the Sabouraud broth and then incubated for 14 days. From n = 22, one sample was subject to SEM evaluation, and from one more sample, dentinal shavings were taken on a Sabouraud dextrose agar plate to confirm the presence of candidal biofilms. The remaining 20 samples were divided (n = 5) and irrigated as follows. Group 1: saline; Group 2: 5.25% NaOCl; Group 3: 0.02% Ag-NPs; Group 4: 0.02% Zn-NPs. The CFUs (colony forming units) were determined per group. SEM imaging of one sample per group was undertaken to correlate the microbiological results. Statistical analysis was conducted using Kruskal–Wallis, with p < 0.05 kept as the significant level. NaOCl reduced the colony counts to a maximum, which was of statistical significance, followed by Ag-NP and Zn-NP. Saline showed the least antimicrobial efficacy. NaOCl was the most effective irrigant against candidal biofilms. Ag-NPs and ZnO-NPs reduced the fungal load but failed to eradicate the biofilm completely. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1812 KB

Open AccessProceeding Paper

The Electrochemical Oxidation of the β-Blocker Drug Propranolol in Biomimetic Media Consisting of Surface-Active Ionic Liquid and a Conventional Cationic Surfactant on a Glassy Carbon Electrode

by Nurendra Chhetri and Moazzam Ali

Eng. Proc. 2023, 59(1), 171; https://doi.org/10.3390/engproc2023059171 - 17 Jan 2024

Cited by 2 | Viewed by 1835

Abstract

Electrochemical studies of the drug and micellar aggregates of surfactants have gained interest in recent years. The aggregation of micelles aims to mimic the structures of biological membranes. It also helps to regulate the pharmacokinetic properties of medicines as they offer a path [...] Read more.

Electrochemical studies of the drug and micellar aggregates of surfactants have gained interest in recent years. The aggregation of micelles aims to mimic the structures of biological membranes. It also helps to regulate the pharmacokinetic properties of medicines as they offer a path for formulations with controlled release abilities. Propranolol (PPL) is a beta blocker drug which is used as a medication in treatments for hypertension, cardiac arrhythmias, and atrial fibrillation, and it is also used to prevent migraines. The electro oxidation of Propranolol was observed using a glassy carbon electrode in cationic surfactants and ionic liquid surfactants with the same chain length using cyclic voltammetry. A well-defined single irreversible peak was found in the potential range of 0.6 to 1.6 V at room temperature. In this paper, Propranolol, in the absence and presence of both the surfactants, is discussed in terms of the pre- and postmicellar phases. The scan rate and effects of both concentrations were evaluated in the presence and absence of surfactants in biphasic surfactant conditions. Diffusion-controlled and irreversible processes involving an adsorption effect were observed in both the surfactants. The interactions of PPL in the presence of different cationic micelles provide an effective approach for estimating the stability of radicals in biological mimetic systems. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1641 KB

Open AccessProceeding Paper

An Elderly Fall Detection System Using Enhanced Random Forest in Machine Learning

by Ravikumar Subburam, Ethirajulu Chandralekha and Vijay Kandasamy

Eng. Proc. 2023, 59(1), 172; https://doi.org/10.3390/engproc2023059172 - 16 Jan 2024

Cited by 7 | Viewed by 3978

Abstract

Fall detection systems play a key role in addressing the health risks faced by elderly individuals. This work implements a fall detection system using machine learning techniques. This work used a fall detection dataset and preprocessed it by encoding categorical variables using one-hot [...] Read more.

Fall detection systems play a key role in addressing the health risks faced by elderly individuals. This work implements a fall detection system using machine learning techniques. This work used a fall detection dataset and preprocessed it by encoding categorical variables using one-hot encoding and handling missing data about ADLs, and associated data were culled from the particular database used. Classifiers such as Support Vector Machine, Logistic Regression, Random Forest, AdaBoost, and Gradient Boosting (GB) were trained and evaluated using the dataset. Training the classifier on a split dataset allows for the evaluation of its performance using a variety of metrics. The components that comprise it are the confusion matrix, the F1 score, recall, accuracy, and precision. Furthermore, in order to determine the major elements that contribute to fall detection, the system displays the importance of certain features. Bar charts showing the relative importance of features, a heatmap showing the confusion matrix, and feature-specific box plots showing the distribution of data are all part of the visualizations included. The ERF model emerged victorious in a comparison of models, achieving the highest level of accuracy. The purpose of this fall detection system is to improve the well-being of the elderly by accurately detecting and reporting instances of falls. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1537 KB

Open AccessProceeding Paper

Towards Comprehensive Home Automation: Leveraging the IoT, Node-RED, and Wireless Sensor Networks for Enhanced Control and Connectivity

by Likewin Thomas, Manoj Kumar MV, Shiva Darshan SL and Prashanth BS

Eng. Proc. 2023, 59(1), 173; https://doi.org/10.3390/engproc2023059173 - 16 Jan 2024

Cited by 9 | Viewed by 6417

Abstract

Automation seems widespread today, yet it is not implemented in daily life. However, most home automation systems are expensive, object-dependent, and lacking in crucial features. The Internet of Things was enabled by this paper’s low-cost home automation system. For development of the IoT, [...] Read more.

Automation seems widespread today, yet it is not implemented in daily life. However, most home automation systems are expensive, object-dependent, and lacking in crucial features. The Internet of Things was enabled by this paper’s low-cost home automation system. For development of the IoT, the system used Node-RED, an open-source platform that uses nodes to visualize tasks. This innovation could operate home devices, including plugs, from anywhere. Wireless sensor network (WSN) technology would record and upload data to the web server from each room. Using the publish-and-subscribe Message Queuing Telemetry Transport (MQTT) protocol, these WSN technologies would communicate. The third feature can modify notifications. In situations of doubt, the house member would be notified by email. This proposal promotes home automation through the IoT. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 3157 KB

Open AccessProceeding Paper

Current Measurement and Fault Detection Based on the Non-Invasive Smart Internet of Things Technique

by Abhrodeep Chanda and Abhishek Gudipalli

Eng. Proc. 2023, 59(1), 174; https://doi.org/10.3390/engproc2023059174 - 17 Jan 2024

Cited by 1 | Viewed by 1937

Abstract

Graphing the consumption of daily essentials like electricity and water is crucial for minimising waste and estimating per-user usage in light of the modern-day data acquisition rally for a better understanding of customer consumption and patterns. Traditional methods of electrical measurement require the [...] Read more.

Graphing the consumption of daily essentials like electricity and water is crucial for minimising waste and estimating per-user usage in light of the modern-day data acquisition rally for a better understanding of customer consumption and patterns. Traditional methods of electrical measurement require the involvement of a trained professional, while more advanced alternatives can be prohibitively expensive or offer limited customisation options. We address the cost factor, flexibility, and complexity issues by using a non-intrusive clamp current transformer around power lines to measure current, estimate power, and upload it to the cloud with proper statistical data. For domestic and industrial applications, the filtered and referenced outputs are read by a low-cost CPU (ultra-low power) equipped with Wi-Fi, an analog-to-digital converter, and Bluetooth capabilities, which then determines the apparent power with an accuracy of 0.37 to 0.8%. Nonlinearity varies from 0.2% to 0.3% as a function of increasing current; nonetheless, offsets are imperceptible under typical operating conditions. Safety in the event of a sudden, large change in the current profile is one of several factors that determine the current measuring limit, together with the rating of the current transformer utilised and other related filtering, reference, calibration, and coding criteria. Our goal is to make the power consumption statistics accessible on the move at little cost by simplifying the circuit and coding of traditional metres. It is smart in that no hard coding is required to send credentials across routers, and fault signals are detected and relayed in accordance with an algorithm. User-specific servers save data for monitoring and conserving energy usage; users do not need to consult specialists or put their own security at risk. Data are acquired from the power line and sent to the cloud where statistical functions are performed to increase insight into consumption and failure. It has impressive range and accuracy in terms of power and current for residential and business applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2302 KB

Open AccessProceeding Paper

Synthesis and Electrochemical Characterization of Activated Porous Carbon Derived from Walnut Shells as an Electrode Material for Symmetric Supercapacitor Application

by Rohit Yadav, Nagaraju Macherla, Kuldeep Singh and Kusum Kumari

Eng. Proc. 2023, 59(1), 175; https://doi.org/10.3390/engproc2023059175 - 17 Jan 2024

Cited by 14 | Viewed by 2617

Abstract

One of the greatest options to address the growing need for hybrid energy storage systems is a supercapacitor with high specific capacitance, high power density, and more charge and discharge cycles. The valorization of walnut shells, a bio waste, into an activated biocarbon [...] Read more.

One of the greatest options to address the growing need for hybrid energy storage systems is a supercapacitor with high specific capacitance, high power density, and more charge and discharge cycles. The valorization of walnut shells, a bio waste, into an activated biocarbon electrode material for the symmetric electric double-layer supercapacitor (EDLC), has been carried out. The valorization method comprises of two-steps for the synthesis of activated biocarbon which are thermal carbonization and ZnCl₂ chemical activation of walnut shells at 700 °C. The sample has good long-term stability and a specific capacitance of 50 Fg⁻¹ @1 Ag⁻¹, making it an excellent supercapacitor electrode material. So, the symmetric electric double-layer capacitor’s (EDLC) promising electrode material was found to be porous AC samples made from walnut shells. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 2704 KB

Open AccessProceeding Paper

Deep Learning-Based Coverless Image Steganography on Medical Images Shared via Cloud

by Ambika, Virupakshappa and Deepak S. Uplaonkar

Eng. Proc. 2023, 59(1), 176; https://doi.org/10.3390/engproc2023059176 - 18 Jan 2024

Cited by 16 | Viewed by 4203

Abstract

Coverless image steganography is an approach for creating images with intrinsic colour and texture information that contain hidden secret information. Recently, generative adversarial networks’ (GANs) deep learning transformers have been used to generate secret hidden images. Although it has been proven that this [...] Read more.

Coverless image steganography is an approach for creating images with intrinsic colour and texture information that contain hidden secret information. Recently, generative adversarial networks’ (GANs) deep learning transformers have been used to generate secret hidden images. Although it has been proven that this approach is resistant to steganalysis attacks, it modifies critical information in the images which makes the images not suitable for applications like disease diagnosis from medical images shared over cloud. The colour and textural modification introduced by GANs affects the feature vector which is extracted from certain image regions and used for disease diagnosis. To solve this problem, this work proposes an attention-guided GAN which transforms images only in certain regions and retains the originality of images in certain regions. Due to this, there is not much distortion to features and disease classification accuracy. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 3423 KB

Open AccessProceeding Paper

Binder Molecular Weight, Concentration, and Flow Rate Optimization for ZnO Nanofiber Synthesis for Electronic Device Applications

by Harshada Mhetre, Vikas Kaduskar, Prashant Chougule, Yogesh Chendake, Nithesh Naik, Pavan Hiremath and Ritesh Bhat

Eng. Proc. 2023, 59(1), 177; https://doi.org/10.3390/engproc2023059177 - 18 Jan 2024

Cited by 2 | Viewed by 1646

Abstract

This study explores the critical factors influencing the properties of zinc oxide nanofibers, which are crucial for their applications in optoelectronics, solar cells, and biomedicine. We specifically investigate the impact of binder molecular weight, binder concentration, and solution flow rate on fiber shape, [...] Read more.

This study explores the critical factors influencing the properties of zinc oxide nanofibers, which are crucial for their applications in optoelectronics, solar cells, and biomedicine. We specifically investigate the impact of binder molecular weight, binder concentration, and solution flow rate on fiber shape, spinning ability, and diameter. By electrospinning zinc acetate, polyvinylpyrrolidone, and dimethylformamide solutions, we demonstrate that the composition, rheological characteristics, and processing parameters significantly affect nanofiber characteristics. We achieved optimized fiber diameters ranging from 24 to 62 nanometers through meticulous parameter modification. Further analysis via FESEM, XRD, and EDS confirms the suitability of these nanofibers for electronic applications, highlighting their potential contributions to the mentioned fields. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1240 KB

Open AccessProceeding Paper

Synthesis, Characterization, and Biological Activity Study of New Heterocyclic Compounds

by Shahad M. Alsafy and Nour Abd Alrazzak

Eng. Proc. 2023, 59(1), 178; https://doi.org/10.3390/engproc2023059178 - 18 Jan 2024

Cited by 7 | Viewed by 5797

Abstract

The synthesis of novel heterocyclic compounds is achieved through a multi-step process involving azo dye (S1), ester (S2), and hydrazide (S3). Initially, azo dye (S1) is synthesized through the reaction between resorcinol and p-aminobenzoic acid. [...] Read more.

The synthesis of novel heterocyclic compounds is achieved through a multi-step process involving azo dye (S1), ester (S2), and hydrazide (S3). Initially, azo dye (S1) is synthesized through the reaction between resorcinol and p-aminobenzoic acid. Subsequently, ester (S2) is formed by reacting azo dye (S1) with concentrated sulfuric acid. Hydrazide (S3) is then synthesized by reacting ester (S2) with 80% hydrazine hydrate. Further reactions of hydrazide (S3) with various anhydrides (maleic anhydride, phthalic anhydride, 3-nitrophthalic anhydride, and succinic anhydride) result in cyclization facilitated by acetic acid, yielding six-membered heterocyclic compounds. Additionally, compound S3 undergoes cyclization with acetyl acetone, ethyl acetoacetate, methyl acetoacetate, and diethyl malonate to produce five-membered heterocyclic compounds. The biological activity of these synthesized compounds is also investigated. Characterization of the prepared compounds is performed using techniques such as Fourier-Transform Infrared Spectroscopy (FT-IR), Proton Nuclear Magnetic Resonance (

^{1}

HNMR), Carbon-13 Nuclear Magnetic Resonance (

^{13}

C-NMR), and Elemental Analysis (CHNS). Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 5111 KB

Open AccessProceeding Paper

Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning

by Sivamani Chinnaswamy, Vigneshwari Natarajan, Selvi Samiappan and Revathy Gurumurthy

Eng. Proc. 2023, 59(1), 179; https://doi.org/10.3390/engproc2023059179 - 18 Jan 2024

Cited by 5 | Viewed by 6676

Abstract

Glaucoma is a condition characterized by unwarranted aqueous humor in the eye, leading to elevated intraocular pressure that can cause damage to the optic nerve. Current treatments for glaucoma are not highly effective and may have significant side effects. Monitoring intraocular pressure in [...] Read more.

Glaucoma is a condition characterized by unwarranted aqueous humor in the eye, leading to elevated intraocular pressure that can cause damage to the optic nerve. Current treatments for glaucoma are not highly effective and may have significant side effects. Monitoring intraocular pressure in real-time and with accuracy is crucial, particularly for patients with severe glaucoma. Therefore, the development of wearable devices for continuous and precise intraocular pressure monitoring is a promising approach for diagnosing and treating glaucoma. However, existing intraocular pressure measurement and monitoring technologies face challenges in terms of scope, exactness, power feasting, and astuteness, which limit their suitability for glaucoma patients. To address these needs, this study focuses on the design and fabrication of an implantable, flexible intraocular pressure sensor capable of long-term continuous monitoring. This research investigates the working principle, structural design, fabrication process, measurement and control system, characterization, and performance testing of the intraocular pressure sensor. This research holds significant importance regarding achieving personalized and accurate treatment for glaucoma patients. Predictions are undertaken using Random forest, and results are obtained. Random forest has the highest accuracy when compared with other state-of-the-art models. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 242 KB

Open AccessProceeding Paper

Rational Wiener Index and Rational Schultz Index of Graphs

by Belman Gautham Shenoy, Raghavendra Ananthapadmanabha, Badekara Sooryanarayana, Prasanna Poojary and Vishu Kumar Mallappa

Eng. Proc. 2023, 59(1), 180; https://doi.org/10.3390/engproc2023059180 - 18 Jan 2024

Viewed by 1382

Abstract

In this research paper, we investigate fundamental graph properties within the context of a simple connected graph denoted as G = (V, E). We introduce the concept of the rational Schultz index. In the context of this paper, our main objective is to [...] Read more.

In this research paper, we investigate fundamental graph properties within the context of a simple connected graph denoted as G = (V, E). We introduce the concept of the rational Schultz index. In the context of this paper, our main objective is to calculate the rational Wiener index and rational Schultz index for a specific class of graphs. Our focus lies in the analysis and computation of these indices within this particular graph family. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

9 pages, 1925 KB

Open AccessProceeding Paper

A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

by Mritunjay Ranjan, Sanjay Tiwari, Arif Md Sattar and Nisha S. Tatkar

Eng. Proc. 2023, 59(1), 181; https://doi.org/10.3390/engproc2023059181 - 17 Jan 2024

Cited by 14 | Viewed by 9378

Abstract

Business and science are using sentiment analysis to extract and assess subjective information from the web, social media, and other sources using NLP, computational linguistics, text analysis, image processing, audio processing, and video processing. It models polarity, attitudes, and urgency from positive, negative, [...] Read more.

Business and science are using sentiment analysis to extract and assess subjective information from the web, social media, and other sources using NLP, computational linguistics, text analysis, image processing, audio processing, and video processing. It models polarity, attitudes, and urgency from positive, negative, or neutral inputs. Unstructured data make emotion assessment difficult. Unstructured consumer data allow businesses to market, engage, and connect with consumers on social media. Text data are instantly assessed for user sentiment. Opinion mining identifies a text’s positive, negative, or neutral opinions, attitudes, views, emotions, and sentiments. Text analytics uses machine learning to evaluate “unstructured” natural language text data. These data can help firms make money and decisions. Sentiment analysis shows how individuals feel about things, services, organizations, people, events, themes, and qualities. Reviews, forums, blogs, social media, and other articles use it. DD (data-driven) methods find complicated semantic representations of texts without feature engineering. Data-driven sentiment analysis is three-tiered: document-level sentiment analysis determines polarity and sentiment, aspect-based sentiment analysis assesses document segments for emotion and polarity, and data-driven (DD) sentiment analysis recognizes word polarity and writes positive and negative neutral sentiments. Our innovative method captures sentiments from text comments. The syntactic layer encompasses various processes such as sentence-level normalisation, identification of ambiguities at paragraph boundaries, part-of-speech (POS) tagging, text chunking, and lemmatization. Pragmatics include personality recognition, sarcasm detection, metaphor comprehension, aspect extraction, and polarity detection; semantics include word sense disambiguation, concept extraction, named entity recognition, anaphora resolution, and subjectivity detection. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1654 KB

Open AccessProceeding Paper

Enhancing User Profile Authenticity through Automatic Image Caption Generation Using a Bootstrapping Language–Image Pre-Training Model

by Smita Bharne and Pawan Bhaladhare

Eng. Proc. 2023, 59(1), 182; https://doi.org/10.3390/engproc2023059182 - 18 Jan 2024

Cited by 4 | Viewed by 1638

Abstract

Generating captions automatically for images has been a challenging task, requiring the integration of image processing and natural language processing techniques. In this study, we propose a system that focuses on generating captions for online social network users’ profile images using a Bootstrapping [...] Read more.

Generating captions automatically for images has been a challenging task, requiring the integration of image processing and natural language processing techniques. In this study, we propose a system that focuses on generating captions for online social network users’ profile images using a Bootstrapping Language–Image Pre-Training Model. Our approach leverages pre-training techniques, enabling the model to learn visual and textual representations from large datasets, which are then fine-tuned on a task-specific dataset. By utilizing this methodology, our proposed system demonstrates promising performance in generating captions for online social network users’ profile images. The model effectively combines visual and textual information to generate informative and contextually relevant captions. This can greatly enhance user engagement and personalization on social media platforms, as users’ profile images are accompanied by meaningful captions that accurately describe the content and context of the images. The proposed system shows its performance on the task of caption generation for online social network users’ profile images. Furthermore, we show that our model can be used to identify scam (fake) profiles on online social networks by generating more accurate and informative captions for real profiles than for fake ones. By leveraging the power of pre-training and bootstrapping techniques, our model showcases its potential in enhancing user experiences, improving platform security, and promoting a more trustworthy online social environment. The proposed system has the potential to improve the authenticity and trustworthiness of user profiles on online social networks. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 2858 KB

Open AccessProceeding Paper

Parametric Optimization of Solar Air Heaters Having Hemispherical Protrusion Roughness in the V-Notch Pattern on the Absorber Plate: A Metaheuristics Optimization Approach

by Premchand Kumar Mahto and Balaram Kundu

Eng. Proc. 2023, 59(1), 183; https://doi.org/10.3390/engproc2023059183 - 18 Jan 2024

Cited by 2 | Viewed by 1885

Abstract

Artificial roughness in the form of protrusions has become a popular technique to improve the thermohydraulic performance of SAHs. So, utmost attention should also be given to determining the suitable parametric values that directly affect the performance of SAHs. Hence, in this work, [...] Read more.

Artificial roughness in the form of protrusions has become a popular technique to improve the thermohydraulic performance of SAHs. So, utmost attention should also be given to determining the suitable parametric values that directly affect the performance of SAHs. Hence, in this work, an attempt has been made to optimize the performance of solar air heaters having hemispherical protrusion roughness in a V-notch pattern on an absorber plate using two different metaheuristic optimization algorithms, i.e., the grey wolf optimization (GWO) algorithm and the dragonfly (DA) algorithm. This study makes use of the correlation equations for the friction factor (f_f) and Nusselt number (Nu), which were developed after conducting the experiments. Four independent parameters, namely the Reynolds number (Re = 3600–21,700), relative protrusion height (e_p/D_h = 0.027–0.069), relative pitch (p/e_p = 6–14), and attack angle (α_a = 15°–75°), were considered to obtain the optimal values of Nu and f_f. In single-objective optimization, the maximization of Nu and the minimization of f_f are two objective functions. The GWO has delivered the best solutions for both objectives with a faster computational rate and less variation. A convergence curve and box plot validated these findings. The maximum value of Nu was found to be 144.567, corresponding to Re = 21,700, e_p/D_h = 0.07, p/e_p = 8.54, and α_a = 75⁰, and the minimum value of f_f was found to be 0.012, corresponding to Re = 21,700, e_p/D_h = 0.03, p/e_p = 14, and α_a = 15°. Pareto multi-objective optimization provides compromised solutions that provide flexibility to the decision maker in selecting a parametric setting. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2227 KB

Open AccessProceeding Paper

Seasonal Analysis of Silicon Photovoltaic Technology Module

by Krupali Kanekar, Prakash Burade and Dhiraj Magare

Eng. Proc. 2023, 59(1), 184; https://doi.org/10.3390/engproc2023059184 - 18 Jan 2024

Viewed by 1544

Abstract

The installed capacity of photovoltaic systems has been rising quickly lately. Deploying photovoltaic systems to generate power, however, is a substantial problem given their reliance on weather and environmental circumstances. The various environmental factors that must be taken into account are temperature, wind [...] Read more.

The installed capacity of photovoltaic systems has been rising quickly lately. Deploying photovoltaic systems to generate power, however, is a substantial problem given their reliance on weather and environmental circumstances. The various environmental factors that must be taken into account are temperature, wind direction, speed, as well as irradiation. The solar system’s standard test condition is never precisely attained outside. Because of this, it is necessary to take into account the seasonal influences to increase solar system performance in a real-time context. In the context of the Indian subcontinent, this research is especially important due to seasonal fluctuations in spectrum-related characteristics. The findings demonstrate that the multi-crystalline technology efficiency and output power evaluated for sites conform to the efficiency as well as output power anticipated using the temperature of the module. Under normal testing conditions, the solar PV module’s parameters are taken from the manufacturer’s datasheet. The accurate modeling of solar systems is necessary to address a variety of PV system problems. We may characterize a solar module’s electrical properties using this precise modeling technique to provide an accurate analysis of cell behavior under any operating situation. Three main stages must be taken into account while modeling a PV cell: the right selection of analogous models, the mathematical formulation of the model, and the precise identification of parameter values in the models. Therefore, in order to mimic the characteristics of solar modules, it is crucial to analyze and design relevant models, as well as use the right modeling technique. The root-mean-square error parameter is considered for the linear regression method. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 1133 KB

Open AccessProceeding Paper

Relevance of Automatic Number Plate Recognition Systems in Vehicle Theft Detection

by Kamlesh Kumawat, Anubha Jain and Neha Tiwari

Eng. Proc. 2023, 59(1), 185; https://doi.org/10.3390/engproc2023059185 - 18 Jan 2024

Cited by 8 | Viewed by 8407

Abstract

Smart vehicle technologies have revolutionized human life in the current era. Smart vehicles, referred to as connected and autonomous vehicles (CAV) are equipped with advanced technologies that increase their safety and security. These technologies have the potential to transform various aspects of society [...] Read more.

Smart vehicle technologies have revolutionized human life in the current era. Smart vehicles, referred to as connected and autonomous vehicles (CAV) are equipped with advanced technologies that increase their safety and security. These technologies have the potential to transform various aspects of society in terms of transformation. This research paper presents an analysis of automatic number plate recognition (ANPR) systems and a comparison at each stage in the aspect of technologies and algorithms involving computer vision. The research paper compares algorithms used for number plate recognition at various ANPR stages. ANPR is also known as the automatic license plate recognition (ALPR) system in many countries. These ANPR systems are generally used in different applications like security surveillance, traffic management, and electric toll collection systems, including law enforcement, parking enforcement, etc. Several factors can destroy the performance of ANPR systems. These factors can lead to inaccuracies in plate recognition or cause the system to fail to identify license plates correctly. Some common factors that can undermine ANPR performance include poor image quality, nonstandard plates, weather conditions, vehicle speed, plate obstructions, lighting conditions, and hardware-based constraints. These challenges make ANPR an interesting area for research. In addition to enhancing the performance of ANPR, other technologies like RFID, and GPS can be used. The paper also focuses on the number plate recognition rate after applying different algorithms. This research aimed to improve the state of knowledge of ANPR, which includes various algorithms and ANPR steps analysis for number plate detection through citing relevant previous work. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 997 KB

Open AccessProceeding Paper

Improvisation in Spinal Surgery Using AR (Augmented Reality), MR (Mixed Reality), and VR (Virtual Reality)

by Dweepna Garg, Nilesh Dubey, Parth Goel, Dipak Ramoliya, Amit Ganatra and Ketan Kotecha

Eng. Proc. 2023, 59(1), 186; https://doi.org/10.3390/engproc2023059186 - 18 Jan 2024

Cited by 13 | Viewed by 2467

Abstract

The day-by-day advancement of extended reality and its subset technologies, along with effective hardware, is increasing their utilization in various sectors like education, training, sports, and healthcare. Healthcare is one domain of concern. Considering this, the main focus of this paper is on [...] Read more.

The day-by-day advancement of extended reality and its subset technologies, along with effective hardware, is increasing their utilization in various sectors like education, training, sports, and healthcare. Healthcare is one domain of concern. Considering this, the main focus of this paper is on spine surgery. In orthopedic surgery, the main uses of virtual reality (VR) are for education, preoperative planning, and intraoperative use. Yet the training imparted still lags. Orthopedic training committees in North America and Europe have endorsed the use of virtual reality for educational purposes. Spinal surgery is one of the main focuses where virtual reality (VR) is applied. In the past, open techniques and instruments that could be seen in real time were used to perform spine surgery. Significant advancements in minimally invasive spine (MIS) surgery have been made. Virtual reality (VR) has been used in preoperative contexts for spine surgery. This paper delves into the applications of augmented reality (AR), virtual reality (VR), and mixed reality (MR) in spinal surgery, emphasizing their potential in education, training, and surgical settings. Specifically, we focus on procedures like pedicle screw placement, cervical spine, and deformity correction, where AR augments surgical precision and information accessibility. The primary objective is to provide a comprehensive framework for evaluating the clinical benefits of AR–VR-enabled spinal surgery technology and propose a viable business model catering to diverse stakeholders, including patients, hospitals, research centers, and technology adopters. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 497 KB

Open AccessProceeding Paper

Blockchain-Enabled Detection of Neurological Disorders Using a Deep Learning Approach

by Kavya Bittasandra Sachidananda Murthy and Sarappadi Narasimha Prasad

Eng. Proc. 2023, 59(1), 187; https://doi.org/10.3390/engproc2023059187 - 18 Jan 2024

Cited by 9 | Viewed by 2938

Abstract

Neurological disorders are a significant health challenge globally, affecting millions of individuals and imposing a considerable economic burden on healthcare systems. Early and accurate diagnosis plays a crucial role in improving patient outcomes and managing these disorders effectively. This abstract presents a novel [...] Read more.

Neurological disorders are a significant health challenge globally, affecting millions of individuals and imposing a considerable economic burden on healthcare systems. Early and accurate diagnosis plays a crucial role in improving patient outcomes and managing these disorders effectively. This abstract presents a novel approach that combines blockchain technology with deep learning algorithms to enhance the detection of neurological disorders. The proposed system leverages the decentralized and transparent nature of blockchain to securely store and share medical data, enabling seamless collaboration among healthcare providers, researchers, and patients. This infrastructure ensures data integrity, privacy, and accessibility, addressing critical concerns in medical data management. Furthermore, the deep learning approach employs advanced neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze large-scale neurological data, including medical images, electroencephalograms (EEGs), and clinical records. By leveraging the power of deep learning, the system can automatically extract relevant features and patterns from complex neurological data, enabling accurate diagnosis and early detection of various disorders. The integration of blockchain and deep learning offers several advantages. Firstly, it facilitates secure and decentralized storage of medical data, ensuring patient privacy and data integrity. Secondly, it enables seamless data sharing and collaboration among multiple stakeholders, promoting knowledge exchange and enhancing research capabilities. Lastly, deep learning algorithms improve the accuracy and efficiency of neurological disorder detection, enabling timely interventions and personalized treatment plans. The proposed system holds great potential in revolutionizing the field of neurological disorder diagnosis and management. By leveraging the combined power of blockchain and deep learning, healthcare providers can enhance their diagnostic capabilities, leading to improved patient outcomes, reduced healthcare costs, and accelerated research advancements. However, further research and development are necessary to address technical challenges, scalability issues, and regulatory considerations to realize the full potential of this innovative approach. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 722 KB

Open AccessProceeding Paper

Investigation of Nano-Composite Dampers Using Different Nanomaterials in Civil Engineering Structures: A Review

by Sandhya. R. Jalgar, Anand M. Hunashyal, Roopa A. Kuri, Madhumati. S. Dhaduti and Shridhar N. Mathad

Eng. Proc. 2023, 59(1), 188; https://doi.org/10.3390/engproc2023059188 - 17 Jan 2024

Cited by 2 | Viewed by 2591

Abstract

Civil engineering structures need to be protected from earthquakes, representing a new area of research that is growing continuously and very rapidly. Design engineers are always searching for lightweight, stronger, and stiffer materials to be applied as vibration-damping materials. Stability in dynamics necessitates [...] Read more.

Civil engineering structures need to be protected from earthquakes, representing a new area of research that is growing continuously and very rapidly. Design engineers are always searching for lightweight, stronger, and stiffer materials to be applied as vibration-damping materials. Stability in dynamics necessitates an active, robust, and convenient mechanism that can absorb the kinetic energy of vibration to prevent the structural system from resonance. Recently, many researchers have successfully used nanomaterials to develop energy-absorbing materials that are lightweight and cost-effective. Traditional damping treatments are based on combinations of viscoelastic, elastomeric, magnetic, and piezoelectric materials. In this paper, a review of various damping techniques for composites made of cement modified by various nanomaterials like Nano Al2O₃ (Aluminum Dioxide), Nano SiO₂ (Silicon Dioxide), Nano TiO₂ (Titanium Dioxide), Graphene, and CNTs (Carbon Nanotubes) is presented. The designs of various nano-composite dampers are presented to strengthen the information progress in this field. The current study’s goal is to discover how nanoparticles impact the cement-based material’s damping properties. The study examined several nanomaterials in cement composites at differing concentrations. With the help of the Dynamic Mechanical Analysis (DMA) method and the Logarithmic Decrement approach, the damping properties of these composites were examined. Scanning Electron Microscopy (SEM) was used to examine the effects of nanomaterials on the microstructure and pore size distribution of the composite. Increasing the quantity of nanoparticles in cement paste may improve its capacity to lessen vibration. The experiments also showed that certain nanomaterials may improve load transmission inside the cement matrix and connect neighboring hydration products, helping to reduce energy loss during the loading process. These nanoparticles will eventually replace the large machinery employed to dampen vibrations in buildings due to their small weight, increased mechanical strength, and effective damping properties. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 1560 KB

Open AccessProceeding Paper

Enhanced Pollutant Adsorption and Antibacterial Activity of a Hydrogel Nanocomposite Incorporating Titanium Dioxide Nanoparticles

by Aseel M. Aljeboree, Zainab D. Alhattab, Sarah A. Hamood, Saif Yaseen Hasan and Ayad F. Alkaim

Eng. Proc. 2023, 59(1), 189; https://doi.org/10.3390/engproc2023059189 - 19 Jan 2024

Cited by 2 | Viewed by 1772

Abstract

This research delineates the synthesis and subsequent application of a hydrogel nanocomposite enriched with titanium dioxide (TiO

_{2}

) nanoparticles as an adsorbent for pollutants and an antibacterial agent. The nanocomposite was prepared using a hydrothermal method, facilitating the efficient incorporation of TiO [...] Read more.

This research delineates the synthesis and subsequent application of a hydrogel nanocomposite enriched with titanium dioxide (TiO

_{2}

) nanoparticles as an adsorbent for pollutants and an antibacterial agent. The nanocomposite was prepared using a hydrothermal method, facilitating the efficient incorporation of TiO

_{2}

nanoparticles. Physicochemical characterizations revealed the nanocomposite’s augmented adsorption capabilities, specifically for pollutants such as Congo red dye (CR), Amoxilline drug (AMX), and Chlorophenol (CPH). Notably, the study demonstrated that the nanocomposite could be completely regenerated and desorbed in water, attesting to its potential for recyclability. The antibacterial potential of the nanocomposite was also investigated, demonstrating significant efficacy against Gram-negative bacteria (E. coli and Klebsiella spp.) compared to Gram-positive strains. The findings of this study emphasize the potential applicability of the hydrogel nanocomposite as an efficient, reusable agent for pollutant removal and antibacterial activity, providing pertinent insights for environmental remediation and biomedical applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 460 KB

Open AccessProceeding Paper

Calculation of Neural Network Weights and Biases Using Particle Swarm Optimization

by Jerin Paul Selvan and Girish Pandurang Potdar

Eng. Proc. 2023, 59(1), 190; https://doi.org/10.3390/engproc2023059190 - 18 Jan 2024

Cited by 2 | Viewed by 3699

Abstract

Various machine learning techniques and algorithms have been used to address, and are still being used to tackle, several real-world issues. One technique that has been extensively employed to address a variety of issues is the usage of neural networks. Neural networks can [...] Read more.

Various machine learning techniques and algorithms have been used to address, and are still being used to tackle, several real-world issues. One technique that has been extensively employed to address a variety of issues is the usage of neural networks. Neural networks can be used to classify data and to calculate regression coefficients. Backpropagation is the cornerstone of neural network training. The process of iteration involves changing the weight of a neural network in response to the rate of error observed in the preceding epoch. The error rates can be reduced and the applicability of the model increased, both of which will increase the model’s dependability. Artificial neural networks are commonly trained using the backpropagation approach, also known as backward propagation of mistakes. This technique aids in figuring out a loss function’s gradient for every network weight. The backpropagation method divides the dataset into training and testing sets. The neural network is assisted in performing exploration and exploitation using a variety of techniques. Among them are algorithms with biological inspiration. By using a different approach, bio-inspired computing can be distinguished from other traditional algorithms. Simple rules and individual life forms or swarms of individuals that adhere to those rules make up the ideology of bio-inspired computing. These living things, also referred to as agents, develop over time and advance with fundamental imperatives. This approach can be categorized as bottom-up or decentralized. In this paper, a neural network is created using weights and biases determined using the swarm’s individual particles. To compare a few parameters between the particle swarm optimization and backpropagation in neural networks, the Pima Indian diabetes dataset is employed. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 2100 KB

Open AccessProceeding Paper

An Artificial Intelligence-Based Scheme for the Management of Vaccines during Pandemics

by Abdul Kareem, Varuna Kumara and Akshatha Naik

Eng. Proc. 2023, 59(1), 191; https://doi.org/10.3390/engproc2023059191 - 19 Jan 2024

Cited by 2 | Viewed by 1297

Abstract

A pandemic like COVID-19 caused a massive blow to the global economy, and its impacts will be large and endure across all domains of life. One of the crucial factors in fighting this pandemic is the proper management and administration of the limited [...] Read more.

A pandemic like COVID-19 caused a massive blow to the global economy, and its impacts will be large and endure across all domains of life. One of the crucial factors in fighting this pandemic is the proper management and administration of the limited vaccines available. The objective of the proposed research is to apply an artificial intelligence approach based on fuzzy logic for the allocation of vaccines to state authorities by a central government. The objective is achieved by developing an artificial intelligence technique based on a fuzzy logic inference system that takes into account the population and the number of active pandemic cases to infer the proportion of available vaccine doses to be allocated to the states. This approach ensures that sufficient doses of vaccines are available in the states on priority where the proportion of the spread is higher and vaccines are not wasted in states where the proportion is lower. The proposed scheme is simulated using MATLAB. The results showed that the proposed artificial intelligence-based approach can ensure proper distribution of the available vaccine doses to the states and enhance the fight against pandemics. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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5 pages, 773 KB

Open AccessProceeding Paper

Synthesis, Characterization, and Biological Activity of a Novel SA-g-p(AAc-co-AM)/ZnO NP Hydrogel Composite

by Aseel M. Aljeboree, Hadeel K. Albdairi, Mohammed Abed Jawad, Sarah A. Hamood, Firas H. Abdulrazzak and Ayad F. Alkaim

Eng. Proc. 2023, 59(1), 192; https://doi.org/10.3390/engproc2023059192 - 19 Jan 2024

Cited by 1 | Viewed by 1257

Abstract

This research investigated the preparation and efficacy of a SA-g-p(AAc-co-AM)/ZnO hydrogel composite with enhanced biological activity. The hydrogel was synthesized using the sodium alginate biopolymer through the co-polymerization method. Our findings indicate that introducing zinc oxide nanoparticles to the hydrogel amplified its biological [...] Read more.

This research investigated the preparation and efficacy of a SA-g-p(AAc-co-AM)/ZnO hydrogel composite with enhanced biological activity. The hydrogel was synthesized using the sodium alginate biopolymer through the co-polymerization method. Our findings indicate that introducing zinc oxide nanoparticles to the hydrogel amplified its biological activity. The disc diffusion technique was applied to evaluate the antimicrobial properties against two Gram-positive bacteria isolates (Staphylococcus aureus, Streptococcus epigenetics) and two Gram-negative bacteria (E.coli, Klebsiella spp.). The antimicrobial activities of three surfaces—ZnO, SA-g-p(AAc-co-AM) hydrogel, and SA-g-p(AAc-co-AM)/ZnO hydrogel composite—were assessed. Characterization of these prepared surfaces was executed using FE-SEM and EDX. The results highlighted that the ZnO NPs exhibited minimal antibacterial activity against both types of bacteria. Conversely, the SA-g-p(AAc-co-AM)/ZnO hydrogel composite demonstrated heightened antibacterial effects against Staphylococcus aureus (30 mm) and Streptococcus epigenetics (25 mm). The Gram-negative bacteria, E.coli and Klebsiella spp., recorded inhibition zones of 13 mm and 12 mm, respectively. The SA-g-p(AAc-co-AM) hydrogel showed diminished antibacterial activity relative to the composite, attributed to the absence of zinc oxide. Overall, the isolated effect of zinc oxide nanoparticles indicated a minimal antibacterial influence on all Gram-positive and Gram-negative bacteria strains. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 2494 KB

Open AccessProceeding Paper

Longevity Recommendation for Root Canal Treatment Using Machine Learning

by Pragati Choudhari, Anand Singh Rajawat and S B Goyal

Eng. Proc. 2023, 59(1), 193; https://doi.org/10.3390/engproc2023059193 - 19 Jan 2024

Cited by 1 | Viewed by 4279

Abstract

Root canal therapy is a vital dental procedure for salvaging severely decayed or infected teeth, preserving them instead of extracting them, thus averting the risk of reinfection. Nonetheless, the prevalence of root canal treatment (RCT) failure is surprisingly high, potentially leading to painful [...] Read more.

Root canal therapy is a vital dental procedure for salvaging severely decayed or infected teeth, preserving them instead of extracting them, thus averting the risk of reinfection. Nonetheless, the prevalence of root canal treatment (RCT) failure is surprisingly high, potentially leading to painful abscesses and severe infections. This study delves into the multifaceted reasons behind RCT failures and employs support vector machine (SVM) technology to predict treatment longevity. The research dataset comprises 332 manual instances, subjected to rigorous 10-fold cross-validation for testing and accuracy assessment. SVM is employed to categorize failed RCT cases into distinct classes, such as broken instruments, periapical radiolucency, root fractures, vertical root fractures, pulp stones, adequate periodontal support, periapical abscesses, overfilled cavities, and perforated or underfilled cavities. By scrutinizing the interplay between these treatment-failure-causing factors, the system discerns their impact on treatment duration. Comparisons are made with other machine learning models, including logistic regression (LR) and the naïve Bayes classifier (NB), to pinpoint the root causes of RCT failure in terms of accuracy, sensitivity, and specificity. Interestingly, logistic regression emerges as the top-performing model, with an impressive 92.47% accuracy rate. This study investigates the causes of RCT failure and employs SVM to predict treatment longevity, offering crucial insights for addressing this common dental issue. This study’s findings highlight the efficacy of logistic regression for identifying RCT failure causes, providing valuable guidance for improving dental procedures and patient outcomes. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 340 KB

Open AccessProceeding Paper

Text Summarization Using Deep Learning Techniques: A Review

by Mohmmadali Muzffarali Saiyyad and Nitin N. Patil

Eng. Proc. 2023, 59(1), 194; https://doi.org/10.3390/engproc2023059194 - 19 Jan 2024

Cited by 8 | Viewed by 15627

Abstract

The process of text summarization is one of the applications of natural language processing that presents one of the most challenging obstacles. This is one of the most challenging duties since it demands an in-depth understanding of the information that is being retrieved [...] Read more.

The process of text summarization is one of the applications of natural language processing that presents one of the most challenging obstacles. This is one of the most challenging duties since it demands an in-depth understanding of the information that is being retrieved from the text; as a result, it is one of the most time-consuming as well. Traditional methods of paraphrasing a text each come with their own individual set of restrictions; this is why it is vital to develop new methods in order to achieve better results in paraphrasing a text. Deep learning has been used, which has resulted in a paradigm shift in the way natural language processing is carried out. The tremendous progress that has been made in the fields of sentiment analysis, text translation, and text summarization can be attributed to the application of methodologies that are based on deep learning. The utilization of these various approaches, which resulted in the production of these advancements, is a primary cause of these breakthroughs. We have outlined a variety of deep learning procedures with the goals of summarizing texts and analyzing details in order to prepare these methods for possible applications in future research. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 963 KB

Open AccessProceeding Paper

Unleashing the Potential of Technology-Driven Learning Management Systems for Student-Centric Excellence to Empower Higher Education

by Abhishika Sharma, Bharti and Anand Pandey

Eng. Proc. 2023, 59(1), 195; https://doi.org/10.3390/engproc2023059195 - 19 Jan 2024

Cited by 8 | Viewed by 2408

Abstract

Since the COVID-19 pandemic, most institutions have adopted online information management systems, also called “learning management systems”. These learning management systems are effective tools for students studying innovative and academic courses as they can host self-learning material, e-tutorials, and online sessions, as well [...] Read more.

Since the COVID-19 pandemic, most institutions have adopted online information management systems, also called “learning management systems”. These learning management systems are effective tools for students studying innovative and academic courses as they can host self-learning material, e-tutorials, and online sessions, as well as assessment processes, such as the online submission of assignments and quizzes. Not only this, but students can also clarify any doubts through the synchronous and asynchronous modes of discussion boards. Learning management system tools have been adopted in all areas of academia post-COVID-19, and now certificates, diplomas, graduations, and post-graduation programs are also being run through online platforms, where working professionals can learn and improve their knowledge and skills in their spare time. This has helped learners in their professional development and other career-related endeavors. All prime universities have tried and adopted online information systems, including viz. flipped classrooms, online e-learning via learning management systems, recorded classes, library records, academic management systems for student performance records, and registration systems. This has become possible due to the widespread adoption of information technology, which has improved communication and bonding among stakeholders via online and internet resources. This comprehensive review aims to identify successful academic tools that top universities have used to popularize online education. This study examines online learning skills, e-flipped classrooms for online systems, e-problem-based learning, assessment evaluation techniques, and outcome-based teaching and learning pedagogy, which are used in online learning systems to enable effective learning among all students. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 531 KB

Open AccessProceeding Paper

A Comprehensive Analysis and Detection Methodology Using Near-Infrared (NIR) Spectroscopy to Unveil the Deceptive Practice of Milk Adulteration

by Geetika Porwal, Kusumlata Jain, Smaranika Mohapatra, Veena Dhayal and Ishita Chopra

Eng. Proc. 2023, 59(1), 196; https://doi.org/10.3390/engproc2023059196 - 22 Jan 2024

Cited by 6 | Viewed by 6545

Abstract

The process of milk adulteration is undergone by adding various substances to milk with the intent of increasing the volume or improving the appearance of the product. The very general adulterants include water, urea, starch, detergent, and even animal fats. This practice is [...] Read more.

The process of milk adulteration is undergone by adding various substances to milk with the intent of increasing the volume or improving the appearance of the product. The very general adulterants include water, urea, starch, detergent, and even animal fats. This practice is harmful to consumers, as it lowers the nutritional value of the milk and exposes them to potential health risks, such as bacterial infections, kidney damage, and gastrointestinal disorders. Milk adulteration is a widespread problem in many countries, particularly in developing nations, where regulations are often lax or poorly enforced. To combat this issue, various measures have been taken, such as implementing stricter regulations and penalties for violators, increasing public awareness about the dangers of contaminated milk, and encouraging farmers to use proper milking and storage practices. Overall, milk adulteration poses a serious threat to public health and safety, and it is essential that consumers remain vigilant and informed about the type of product being consumed. In the current study, it is observed that dairies and milk farms are using adulterants to such an extensive amount that it is leading to various health issues, as milk is used as a product by every age of the human race. The adulterant used in the current study was urea with a concentration of 10%. The NIR spectroscopy used in the study was used as a tool to identify the difference between an unadulterated and adulterated milk samples. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 887 KB

Open AccessProceeding Paper

Leveraging ChatGPT for Empowering MSMEs: A Paradigm Shift in Problem Solving

by Gautam Bapat, Rinku Mahindru, Anuj Kumar, Aruna Dev Rroy, Sanjay Bhoyar and Sonia Vaz

Eng. Proc. 2023, 59(1), 197; https://doi.org/10.3390/engproc2023059197 - 22 Jan 2024

Cited by 7 | Viewed by 5461

Abstract

This paper delves into the potential of harnessing ChatGPT, an AI-driven language model, to empower micro, small, and medium enterprises (MSMEs) by revolutionising their approach to problem solving. The research aims to explore the integration of ChatGPT into MSME operations and evaluate its [...] Read more.

This paper delves into the potential of harnessing ChatGPT, an AI-driven language model, to empower micro, small, and medium enterprises (MSMEs) by revolutionising their approach to problem solving. The research aims to explore the integration of ChatGPT into MSME operations and evaluate its impact on enhancing their problem-solving efficiency. By scrutinising the literature and reviewing several case studies, a comprehensive framework emerges, detailing the utilisation of ChatGPT as a problem-solving tool for MSMEs. This involves training the model with industry-specific data and incorporating it into MSME communication channels, enabling intelligent responses to queries. The results highlight the substantial improvement in problem-solving capabilities, with the model’s real-time assistance diminishing response time, elevating accuracy, and furnishing tailored solutions to intricate challenges. However, limitations arise from the model’s reliance on existing data, potentially introducing biases. Significantly, this research offers practical implications for both MSMEs and policymakers. ChatGPT’s integration holds promise in terms of heightened efficiency, productivity, and competitiveness for MSMEs, counteracting resource constraints, and fostering growth. Policymakers can aid this transition by formulating ethical guidelines to ensure the equitable and transparent application of AI in the MSME sector. This study’s novelty lies in its focus on MSME empowerment through ChatGPT integration, bridging a research gap. Its value emanates from the actionable insights provided, offering guidance to MSMEs, policymakers, and practitioners keen on leveraging AI-driven solutions to amplify problem-solving capacities within the realm of MSMEs. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1988 KB

Open AccessProceeding Paper

Diagnosis of Autism in Children Using Deep Learning Techniques by Analyzing Facial Features

by Pranavi Reddy and Andrew J

Eng. Proc. 2023, 59(1), 198; https://doi.org/10.3390/engproc2023059198 - 22 Jan 2024

Cited by 38 | Viewed by 14372

Abstract

Autism spectrum disorder (ASD) is a complex neurological disorder that results in aberrant personality traits, cognitive function, and interpersonal relationships. It impacts the child’s linguistic and social skills, interaction abilities, and capacity for logical thought. It is possible to use the human face [...] Read more.

Autism spectrum disorder (ASD) is a complex neurological disorder that results in aberrant personality traits, cognitive function, and interpersonal relationships. It impacts the child’s linguistic and social skills, interaction abilities, and capacity for logical thought. It is possible to use the human face as a physiological identifier since it can serve as an indicator of brain function, thus helping with early diagnosis in a simple and effective way. The purpose of this study is to detect autism from facial images using a deep learning model. To accurately identify autism in children, we used three pre-trained CNN models, VGG16, VGG19 and, EfficientnetB0, as feature extractors and binary classifiers. The suggested models were trained using a publicly available dataset from Kaggle that included 3014 images of children characterized as autistic and non-autistic. The models yielded accuracies of 84.66%, 80.05%, and 87.9%, respectively. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1139 KB

Open AccessProceeding Paper

Seasonal Variation in Land Surface Temperature in Babylon Governorate: A Remote Sensing and GIS Analysis

by Maysaa M. K. Hussain and Ameerah Ab. Al-Sadooni

Eng. Proc. 2023, 59(1), 199; https://doi.org/10.3390/engproc2023059199 - 22 Jan 2024

Cited by 1 | Viewed by 1213

Abstract

The primary objective of this research is to quantify the land surface temperature (LST) across different seasons in the Babylon Governorate for the year 2022. Utilizing data from LANDSAT 8, the study focuses on three key spectral bands: thermal, red, and near-infrared (NIR) [...] Read more.

The primary objective of this research is to quantify the land surface temperature (LST) across different seasons in the Babylon Governorate for the year 2022. Utilizing data from LANDSAT 8, the study focuses on three key spectral bands: thermal, red, and near-infrared (NIR) bands. The spatial distribution maps of LST are generated for winter, spring, summer, and autumn, and descriptive statistics are employed to characterize the LST features for each seasonal map. The findings reveal a significant seasonal variation in LST. Specifically, the maximum temperature recorded in summer is approximately three times higher than that recorded in winter, with a difference of nearly 46

^{°}

C. Conversely, the minimum temperature varies from 18

^{°}

C in summer to approximately 4.5

^{°}

C in winter. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 4705 KB

Open AccessProceeding Paper

The Effect of Metal Filler on the Mechanical Performance of Epoxy Resin Composites

by Bhavith K, Prashanth Pai M, Sudheer M, Ramachandra C G, Maruthi Prashanth B H and Kiran Kumar B

Eng. Proc. 2023, 59(1), 200; https://doi.org/10.3390/engproc2023059200 - 18 Jan 2024

Cited by 20 | Viewed by 5373

Abstract

It is a common practice in the plastics industry to compound polymers with fillers to reduce the manufacturing cost and/or attain desired properties. By combining different fillers with various polymer matrices, polymer composites can be tailored to achieve property combinations which cannot easily [...] Read more.

It is a common practice in the plastics industry to compound polymers with fillers to reduce the manufacturing cost and/or attain desired properties. By combining different fillers with various polymer matrices, polymer composites can be tailored to achieve property combinations which cannot easily be obtained from either the polymer matrices or the reinforcements alone. In the past decades, different metallic (Cu, Al, Steel, etc.) and ceramic fillers (SiC, Al₂O₃, CuO, TiC, TiO₂, TiN, ZrO₂, ZnO, ZnF₂, SiO₂, etc.) have been used as reinforcements in composite preparation because of their effectiveness in reinforcing polymers. In light of the above, this research is aimed at the fabrication and study of the basic mechanical properties of epoxy-resin composites filled with different weight percentages of metal filler. It includes the study of the mechanical properties of cast-iron-filler-reinforced epoxy-based polymer matrix composites. Epoxy composites containing cast iron in different weight percentages are prepared using casting technique. Data on neat epoxy are also included for comparison. All the tests were conducted at room temperature and according to ASTM standards. Density, hardness (Rockwell), tensile, flexural and impact tests were conducted, and the data were analyzed with the help of statistical charts to draw useful inferences. It was observed that the inclusion of cast iron filler affected most of the mechanical properties of neat epoxy. The density, hardness, impact strength, tensile and flexural properties of the developed composites exhibited a varying trend with respect to cast iron content. The increase in cast iron content showed significant improvement in tensile properties, hardness, impact strength and the density of the composites. The flexural strength was found to decrease at a higher cast iron content. This research also highlights the possible reasons for variation in the mechanical properties of developed polymer composites. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 4556 KB

Open AccessProceeding Paper

Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture

by Gopalakrishnan Nagaraj, Dakshinamurthy Sungeetha, Mohit Tiwari, Vandana Ahuja, Ajit Kumar Varma and Pankaj Agarwal

Eng. Proc. 2023, 59(1), 201; https://doi.org/10.3390/engproc2023059201 - 22 Jan 2024

Cited by 10 | Viewed by 3131

Abstract

Insects and illnesses that affect plants can have a major negative effect on both their quality and their yield. Digital image processing may be applied to diagnose plant illnesses and detect plant pests. In the field of digital image processing, recent developments have [...] Read more.

Insects and illnesses that affect plants can have a major negative effect on both their quality and their yield. Digital image processing may be applied to diagnose plant illnesses and detect plant pests. In the field of digital image processing, recent developments have shown that more conventional methods have been eclipsed by deep learning by a wide margin. Now, researchers are concentrating their efforts on the question of how the technique of deep learning may be applied to the issue of identifying plant diseases and pests. In this paper, the difficulties that arise when diagnosing plant pathogens and pests are outlined, and the various diagnostic approaches that are currently in use are evaluated and contrasted. This article presents a summary of three perspectives, each of which is based on a different network design, in recent research on deep learning applied to the detection of plant diseases and pests. We developed a convolutional neural network (CNN)-based framework for identifying pest-borne diseases in tomato leaves using the Plant Village Dataset and the MobileNetV2 architecture. We compared the performance of our proposed MobileNetV2 model with other existing methods and demonstrated its effectiveness in pest detection. Our MobileNetV2 model achieved an impressive accuracy of 93%, outperforming some other models like GoogleNet and VGG16, which were fully trained on the pest dataset in terms of speed. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1141 KB

Open AccessProceeding Paper

Bond Graph Modeling and Simulation of Hybrid Piezo-Flexural-Hydraulic Actuator

by Rudraksha Kelkar, Mohith Santhya, Mithun Kanchan and Omkar S. Powar

Eng. Proc. 2023, 59(1), 202; https://doi.org/10.3390/engproc2023059202 - 19 Jan 2024

Cited by 1 | Viewed by 1569

Abstract

In this study, a hybrid piezo-flexural-hydraulic actuator is modeled and simulated using bond graph methodology. The hybrid actuator comprises piezoelectric stack actuator, mechanical flexural amplifier, and hydraulic piston actuator. The piezoelectric stack actuator produces electrically controllable displacement. This displacement is amplified by a [...] Read more.

In this study, a hybrid piezo-flexural-hydraulic actuator is modeled and simulated using bond graph methodology. The hybrid actuator comprises piezoelectric stack actuator, mechanical flexural amplifier, and hydraulic piston actuator. The piezoelectric stack actuator produces electrically controllable displacement. This displacement is amplified by a cascading combination of flexural amplifier and hydraulic actuator. A domain-independent bond graph model for the proposed hybrid actuator is developed. Using this bond graph, a mathematical model and a state space representation for the hybrid actuator are derived. The bond graph model is simulated using a 20-sim bond graph simulation software. The results of the simulation provide displacement characteristics and sensitivity analysis for each component and the hybrid actuator as a whole. The study plays a significant role in understanding the dynamic behavior of a multi-domain system using the bond graph methodology. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1238 KB

Open AccessProceeding Paper

Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos

by T. Gopalakrishnan, Naynika Wason, Raguru Jaya Krishna, Vamshi Krishna B and N. Krishnaraj

Eng. Proc. 2023, 59(1), 203; https://doi.org/10.3390/engproc2023059203 - 22 Jan 2024

Cited by 4 | Viewed by 7238

Abstract

Human Action Recognition is considered to be a critical problem and it is always a challenging issue in computer vision applications, especially video surveillance applications. State-of-the-art classifiers introduced to solve the problem are computationally expensive to train and require very large amounts of [...] Read more.

Human Action Recognition is considered to be a critical problem and it is always a challenging issue in computer vision applications, especially video surveillance applications. State-of-the-art classifiers introduced to solve the problem are computationally expensive to train and require very large amounts of data. In this paper, we solve the problems of low data and resource availability in surveillance datasets by employing transfer learning and fine-tuning the Inflated 3D CNN model and the SlowFast Network model to automatically extract features from surveillance videos in the SPHAR dataset for classification into respective action classes. This approach works well to process the spatio-temporal nature of videos. Fine-tuning is carried out in the networks by replacing the last classification (dense) layer as per the available number of classes in the constructed new dataset. We ultimately compare the performance of both fine-tuned networks by taking accuracy as the metric, and find that the I3D model performs better for our use-case. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 855 KB

Open AccessProceeding Paper

Unlocking Brand Excellence: Harnessing AI Tools for Enhanced Customer Engagement and Innovation

by Anuj Kumar, Gautam Bapat, Arya Kumar, Sweta Leena Hota, G. David Abishek and Sonia Vaz

Eng. Proc. 2023, 59(1), 204; https://doi.org/10.3390/engproc2023059204 - 22 Jan 2024

Cited by 18 | Viewed by 6802

Abstract

This research article delves into the integration of AI tools, particularly Chat GPT, within brand marketing strategies, aiming to uncover their practical applications and associated benefits and challenges. Real-world case studies, practical recommendations, and insights into AI-driven innovation collectively form a guide for [...] Read more.

This research article delves into the integration of AI tools, particularly Chat GPT, within brand marketing strategies, aiming to uncover their practical applications and associated benefits and challenges. Real-world case studies, practical recommendations, and insights into AI-driven innovation collectively form a guide for brand managers aspiring to leverage these tools effectively. The research findings highlight Chat GPT’s transformative potential, showcasing successful integration into marketing strategies that enhance customer experiences, streamline interactions, and introduce innovative campaigns. Despite acknowledging the dynamic nature of AI technology and potential biases in data analysis, the article provides practical recommendations for brand managers, emphasizing ethical considerations and adapting to the evolving AI landscape. The research underscores the importance of responsible AI usage, transparency, and continuous adaptation to changing consumer behaviors for maintaining trust and ethical standards. This contribution to the existing literature combines real-world examples, practical insights, and a mixed-methods approach, offering a unique perspective on how AI, particularly Chat GPT, can reshape customer engagement, brand communication, and creativity in both academic and industrial contexts. The article provides a comprehensive examination of AI tools’ practical utility, bridging theory and application for a nuanced understanding in the field of brand marketing. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 840 KB

Open AccessProceeding Paper

Multimodal Deep Learning in Early Autism Detection—Recent Advances and Challenges

by Sheril Sophia Dcouto and Jawahar Pradeepkandhasamy

Eng. Proc. 2023, 59(1), 205; https://doi.org/10.3390/engproc2023059205 - 23 Jan 2024

Cited by 29 | Viewed by 15042

Abstract

Autism spectrum disorder (ASD) is a global concern, with a prevalence rate of approximately 1 in 36 children according to estimates from the Centers for Disease Control and Prevention (CDC). Diagnosing ASD poses challenges due to the absence of a definitive medical test. [...] Read more.

Autism spectrum disorder (ASD) is a global concern, with a prevalence rate of approximately 1 in 36 children according to estimates from the Centers for Disease Control and Prevention (CDC). Diagnosing ASD poses challenges due to the absence of a definitive medical test. Instead, doctors rely on a comprehensive evaluation of a child’s developmental background and behavior to reach a diagnosis. Although ASD can occasionally be identified in children aged 18 months or younger, a reliable diagnosis by an experienced professional is typically made by the age of two. Early detection of ASD is crucial for timely interventions and improved outcomes. In recent years, the field of early diagnosis of ASD has been greatly impacted by the emergence of deep learning models, which have brought about a revolution by greatly improving the accuracy and efficiency of ASD detection. The objective of this review paper is to examine the recent progress in early ASD detection through the utilization of multimodal deep learning techniques. The analysis revealed that integrating multiple modalities, including neuroimaging, genetics, and behavioral data, is key to achieving higher accuracy in early ASD detection. It is also evident that, while neuroimaging data holds promise and has the potential to contribute to higher accuracy in ASD detection, it is most effective when combined with other modalities. Deep learning models, with their ability to analyze complex patterns and extract meaningful features from large datasets, offer great promise in addressing the challenge of early ASD detection. Among various models used, CNN, DNN, GCN, and hybrid models have exhibited encouraging outcomes in the early detection of ASD. The review highlights the significance of developing accurate and easily accessible tools that utilize artificial intelligence (AI) to aid healthcare professionals, parents, and caregivers in early ASD symptom recognition. These tools would enable timely interventions, ensuring that necessary actions are taken during the initial stages. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 3459 KB

Open AccessProceeding Paper

Energy Management Control Strategy Based on Harris Hawks Optimization Technique for Fuel Cell Hybrid Electric Vehicle

by Gondu Vykunta Rao, Ankit Soni, Aruna Bharathi, Baratam Murali and Vanjarapu Vykunta Rao

Eng. Proc. 2023, 59(1), 206; https://doi.org/10.3390/engproc2023059206 - 23 Jan 2024

Cited by 5 | Viewed by 1639

Abstract

The focus and sales of EVs are slowly coming into scope, as the power source of such vehicles is a significant area in which the integration of power systems is becoming a crucial issue. This work involves the use of hybrid sources, batteries [...] Read more.

The focus and sales of EVs are slowly coming into scope, as the power source of such vehicles is a significant area in which the integration of power systems is becoming a crucial issue. This work involves the use of hybrid sources, batteries as a primary source, fuel cells, and an ultra-capacitor as an auxiliary source. This hybrid system provides the grip of the FCEV. The constraints of fuel cells are the SOC of the battery and the H2 level. These three power sources in hybrid systems are connected to the DC bus via proper DC-to-DC converters. This paper will discuss the combination of Harris Hawks Optimization (HHO) for the energy management and control of these source systems, for the constraint of mandated sources, and to ensure stability. The proposed system provides a satisfactory energy management system for the hybrid system. Using the proposed technique, the fuel consumption settling period is reduced. The proposed method was implemented and validated with and without the HHO technique. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 746 KB

Open AccessProceeding Paper

Metaverse Unleashed: Augmenting Creativity and Innovation in Business Education

by Rinku Mahindru, Anuj Kumar, Gautam Bapat, Aruna Dev Rroy, Kavita and Nancy Sharma

Eng. Proc. 2023, 59(1), 207; https://doi.org/10.3390/engproc2023059207 - 23 Jan 2024

Cited by 14 | Viewed by 3445

Abstract

This research paper explores integrating the metaverse into business education for fostering creativity and innovation. It examines benefits, challenges, and implications, focusing on enhancing learning experiences. The study adopts qualitative research, incorporating articles, a literature review, and case studies on business education. It [...] Read more.

This research paper explores integrating the metaverse into business education for fostering creativity and innovation. It examines benefits, challenges, and implications, focusing on enhancing learning experiences. The study adopts qualitative research, incorporating articles, a literature review, and case studies on business education. It analyses metaverse applications in promoting creativity and innovation. Incorporating the metaverse significantly enhances student engagement, critical thinking, collaboration, and problem-solving skills. Challenges include technological infrastructure, ethics, and faculty development. Further research is needed to address these limitations and maximize metaverse benefits. Valuable insights are offered to educators, institutions, and policymakers. Strategies for integrating the metaverse, designing immersive learning experiences, and fostering creativity and innovation are provided. Policies and guidelines can be developed for effective metaverse adoption. This research contributes by focusing on the metaverse’s role in business education. It synthesizes current knowledge, identifies gaps, and offers practical implications for dynamic learning environments. It emphasizes creativity and innovation incorporation in the educational process. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 241 KB

Open AccessProceeding Paper

Hybrid Spectrum Inversion and Dispersion Compensation for Mitigating Fiber Losses in Optical Systems

by Zainab A. Abbas, Ibrahim A. Murdas and Talib M. Abbas

Eng. Proc. 2023, 59(1), 208; https://doi.org/10.3390/engproc2023059208 - 23 Jan 2024

Cited by 1 | Viewed by 1585

Abstract

Optical fiber systems are integral to various applications ranging from telecommunications to medical technologies. These systems, however, face significant challenges due to power losses and nonlinear phase changes that occur as signals propagate through the fiber core. To address these issues, this study [...] Read more.

Optical fiber systems are integral to various applications ranging from telecommunications to medical technologies. These systems, however, face significant challenges due to power losses and nonlinear phase changes that occur as signals propagate through the fiber core. To address these issues, this study introduces a hybrid optical method combining spectrum inversion and fiber dispersion compensation. The primary objective is to enhance system performance by mitigating both linear and nonlinear fiber losses. A comparative evaluation reveals that employing optical phase conjugation in the system leads to a substantial improvement in performance metrics. Specifically, the Q-Factor, a measure of signal quality, increases to 20 at an input laser power of 5 mw when using the proposed method, compared to a Q-Factor of 5 achieved with traditional methods. The findings highlight the effectiveness of the proposed technique in compensating for fiber losses and suggest its potential utility in improving the performance of optical fiber systems across various applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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14 pages, 1063 KB

Open AccessProceeding Paper

Redefining Workspaces: Young Entrepreneurs Thriving in the Metaverse’s Remote Realm

by Rinku Mahindru, Gautam Bapat, Punam Bhoyar, G. David Abishek, Anuj Kumar and Sonia Vaz

Eng. Proc. 2023, 59(1), 209; https://doi.org/10.3390/engproc2023059209 - 23 Jan 2024

Cited by 5 | Viewed by 2985

Abstract

This research paper explores the intersection of the Metaverse and remote working, specifically concerning young entrepreneurs. Its primary objective is to examine the opportunities and challenges presented by the Metaverse for this demographic engaged in remote work, providing actionable insights for both practitioners [...] Read more.

This research paper explores the intersection of the Metaverse and remote working, specifically concerning young entrepreneurs. Its primary objective is to examine the opportunities and challenges presented by the Metaverse for this demographic engaged in remote work, providing actionable insights for both practitioners and policymakers. The methodology employed involves an extensive literature review that delves into the concept of the Metaverse, its evolution, and the implications it holds for remote working. This foundational exploration is supplemented by in-depth analyses of case studies and examples, offering real-life illustrations of how young entrepreneurs leverage the Metaverse for remote work. The findings of this investigation reveal a landscape ripe with potential for young entrepreneurs operating within the Metaverse. This study highlights the benefits of enhanced collaboration, expanded global market access, and the emergence of innovative augmented and virtual reality applications. However, these opportunities are accompanied by notable challenges, including issues related to technological infrastructure readiness, security concerns, and potential societal impacts. Acknowledging the evolving nature of the Metaverse concept and potential biases in sample selection are critical research limitations. Practically, this paper translates its findings into actionable recommendations for young entrepreneurs seeking to maximize their utilization of the Metaverse for remote work. It emphasizes the importance of skill acquisition, adaptability to the changing work environment, and the implementation of robust security measures. Furthermore, it advocates for policymakers to develop supportive regulations and policies that recognize and accommodate the intricacies of virtual contracts, data protection, and cross-border collaborations. Strengthening intellectual property laws and tailoring taxation policies for this digital domain are also crucial aspects. In essence, this research contributes significantly by synthesizing the existing literature, presenting real-world examples, and offering practical insights tailored to the unique space where the Metaverse and remote work intersect. Its value lies in bridging gaps in understanding, providing actionable guidance, and contributing to the evolving discourse on this emerging field. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 3650 KB

Open AccessProceeding Paper

Traffic Management System Using YOLO Algorithm

by Pankaj Kunekar, Yogita Narule, Richa Mahajan, Shantanu Mandlapure, Eshan Mehendale and Yashashri Meshram

Eng. Proc. 2023, 59(1), 210; https://doi.org/10.3390/engproc2023059210 - 23 Jan 2024

Cited by 19 | Viewed by 12731

Abstract

The issue of traffic congestion is becoming worse day by day. The typical traffic lights are unable to effectively regulate the growing number of vehicular traffic; therefore, we mixed computer vision and machine learning to mimic complicated incoming traffic at signalized intersections. This [...] Read more.

The issue of traffic congestion is becoming worse day by day. The typical traffic lights are unable to effectively regulate the growing number of vehicular traffic; therefore, we mixed computer vision and machine learning to mimic complicated incoming traffic at signalized intersections. This was accomplished using the cutting-edge, real-time object detection system You Only Look Once (YOLO), which is built on deep convolutional neural networks. In order to maximize the number of vehicles that can cross safely with the least amount of waiting time, this paper presents an efficient method to use this algorithm, where traffic signal phases are based on the data obtained, primarily queue density and waiting time per vehicle. Embedded controllers that adopt the transfer learning methodology can implement YOLO. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 644 KB

Open AccessProceeding Paper

Sustainable Green Manufacturing Approaches in India—A Step towards a New Green Revolution through SMEs

by Gautam S. Bapat, Anuj Kumar, Arya Kumar, Sweta Leena Hota, Kavita and Komal Singh

Eng. Proc. 2023, 59(1), 211; https://doi.org/10.3390/engproc2023059211 - 23 Jan 2024

Cited by 7 | Viewed by 6202

Abstract

Sustainable production minimises resource use, removes toxic substances, and generates zero waste, reducing greenhouse gas emissions throughout the product and service life cycle. Most of the world’s manufacturing will be in Asia in the next 20 years, generating numerous opportunities on this continent. [...] Read more.

Sustainable production minimises resource use, removes toxic substances, and generates zero waste, reducing greenhouse gas emissions throughout the product and service life cycle. Most of the world’s manufacturing will be in Asia in the next 20 years, generating numerous opportunities on this continent. Sustainable manufacturing must respond to economic issues by developing wealth and new services that assure long-term development and competitiveness while minimising environmental harm and addressing social concerns. Green manufacturing is a sustainable manufacturing strategy that may alleviate several challenges in Indian SMEs. Due to different causes, SMEs may have been previously exempt from regulatory and societal constraints. It is time to stop disregarding SMEs’ environmental implications. SMEs are vital to economies but have a harmful influence on the environment if they do not follow green practices. This review examines the present condition of green manufacturing in India. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2132 KB

Open AccessProceeding Paper

An Efficient Routing Algorithm for Implementing Internet-of-Things-Based Wireless Sensor Networks Using Dingo Optimizer

by K. Kishore Kumar and G. Sreenivasulu

Eng. Proc. 2023, 59(1), 212; https://doi.org/10.3390/engproc2023059212 - 24 Jan 2024

Cited by 9 | Viewed by 1555

Abstract

For Internet of Things wireless sensor networks (IOT WSNs), we suggest an energy-efficient cluster-based routing protocol. The primary issues that restrict the lifespan of a sensor network are the limited battery life of sensor nodes and ineffective protocols. Our goal is to offer [...] Read more.

For Internet of Things wireless sensor networks (IOT WSNs), we suggest an energy-efficient cluster-based routing protocol. The primary issues that restrict the lifespan of a sensor network are the limited battery life of sensor nodes and ineffective protocols. Our goal is to offer a green routing protocol that wireless sensor networks can use. We present a novel approach to routing and data collection using network clustering, utilizing a modified version of the Dingo Optimizer. The main accomplishment of our suggested strategy is the elimination of the superfluous overhead with the use of cluster-head selection based on the Dingo Optimizer. Each sensor node has a data-compression method in place, which reduces the energy consumption and lengthens the lifespan of the IOT network. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1097 KB

Open AccessProceeding Paper

Eco-Friendly Adsorption of Cationic (Methylene Blue) and Anionic (Congo Red) Dyes from Aqueous Solutions Using Sawdust

by Aseel M. Aljeboree, Firas H. Abdulrazzak, Zuhra Muter Saleh, Hussein Abdullah Abbas and Ayad F. Alkaim

Eng. Proc. 2023, 59(1), 213; https://doi.org/10.3390/engproc2023059213 - 24 Jan 2024

Cited by 7 | Viewed by 2428

Abstract

In this study, sawdust (SWS) was employed as an eco-friendly and low-cost adsorbent for the removal of anionic (Congo red, CR) and cationic (methylene blue, MB) dyes from aqueous solutions at 25 °C. The investigation encompasses various parameters affecting the adsorption process, including [...] Read more.

In this study, sawdust (SWS) was employed as an eco-friendly and low-cost adsorbent for the removal of anionic (Congo red, CR) and cationic (methylene blue, MB) dyes from aqueous solutions at 25 °C. The investigation encompasses various parameters affecting the adsorption process, including weight of sawdust adsorbent, pH, initial dye concentration, and equilibrium time. Characterization techniques such as Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM) were conducted for an in-depth understanding of the adsorption mechanism. Optimal conditions were found to be SWS weight of 0.1 g/L, dye concentration of 15 mg/L, and equilibrium time of 1 h. Under these conditions, removal percentages of 95.88% for MB and 67.78% for CR were achieved, with adsorption capacities of 14.35 mg/g and 10.22 mg/g, respectively. The results demonstrate that SWS, though considered waste, has significant potential as a low-cost adsorbent for dye removal from aqueous solutions. Removal efficiency increased with SWS weight, ranging from 75.54% to 98.50% for MB, and 50.86% to 80.012% for CR, while adsorption capacity (Qe) inversely correlated with surface weight, ranging from 45.55 to 9.12 mg/g for MB, and 15.23 to 8.076 mg/g for CR. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 726 KB

Open AccessProceeding Paper

A Grey Wolf Optimisation-Based Framework for Emotion Recognition on Electroencephalogram Data

by Ram Avtar Jaswal and Sunil Dhingra

Eng. Proc. 2023, 59(1), 214; https://doi.org/10.3390/engproc2023059214 - 24 Jan 2024

Viewed by 1444

Abstract

Human emotions trigger reflective transformations within the brain, leading to unique patterns of neural activity and behaviour. This study connects the power of electroencephalogram (EEG) data to investigate the intricate impacts of emotions, considering their reflective significance in our daily lives, in depth. [...] Read more.

Human emotions trigger reflective transformations within the brain, leading to unique patterns of neural activity and behaviour. This study connects the power of electroencephalogram (EEG) data to investigate the intricate impacts of emotions, considering their reflective significance in our daily lives, in depth. The versatile applications of EEG signals encompass an array of domains, from the categorisation of motor imagery activities to the control of advanced prosthetic devices. However, EEG data present a difficult challenge due to their inherent noisiness and non-stationary nature, making it imperative to extract salient features for classification purposes. In this paper, we introduce a novel and effective framework reinforced by Grey Wolf Optimisation (GWO) for the recognition and interpretation of EEG signals of emotion dataset. The core objective of our research is to unravel the intricate neural signatures that underlie emotional experiences and pave the way for more nuanced emotion recognition systems. To measure the efficacy of our proposed framework, we conducted experiments utilising EEG recordings from a unit of 32 participants. During the experiments, participants were exposed to emotionally charged video stimuli, each lasting one minute. Subsequently, the collected EEG data of emotion were meticulously analysed, and a support vector machine (SVM) classifier was employed for the robust categorisation of the extracted EEG features. Our results underscore the potential of the GWO-based framework, achieving an impressive accuracy rate of 93.32% in accurately identifying and categorising emotional states. This research not only provides valuable insights into the neural underpinnings of emotions but also lays a solid foundation for the development of more sophisticated and emotionally intelligent human–computer interaction systems. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 3379 KB

Open AccessProceeding Paper

Finite Element Study on Coconut Inflorescence Stem Fiber Composite Panels Subjected to Static Loading

by Muralidhar Nagarajaiah, Arunkumar Yadav, Shanmukha Prasannakumar, Raveesh Ranganathapura Mahadevaiah and Pavan Hiremath

Eng. Proc. 2023, 59(1), 215; https://doi.org/10.3390/engproc2023059215 - 24 Jan 2024

Cited by 1 | Viewed by 1578

Abstract

Natural fiber-reinforced composites (NFCs) are alternatives to synthetic fiber-reinforced composites, since they are abundant in nature, inexpensive, lightweight, and have a high strength-to-weight ratio. Natural fibers encompass a diverse composition, including lignin, hemicellulose, wax, and cellulose. Natural fibers are environmentally friendly, biodegradable, renewable, [...] Read more.

Natural fiber-reinforced composites (NFCs) are alternatives to synthetic fiber-reinforced composites, since they are abundant in nature, inexpensive, lightweight, and have a high strength-to-weight ratio. Natural fibers encompass a diverse composition, including lignin, hemicellulose, wax, and cellulose. Natural fibers are environmentally friendly, biodegradable, renewable, reusable, and sustainable. In bio-composites, natural fibers such as jute, banana, hemp, coir, kenaf, areca nut, and coconut inflorescence stem fibers, are blended with resin. Natural fiber-reinforced bio-composites have various applications in the construction industry, automobile industry, aerospace industry, sports equipment and gadgets, textile industry, and hotel industry. Fibers from natural sources are also used as reinforcements in composites, such as roofing sheets, bricks, door panels, furniture panels, and panels for interior decoration. The mechanical properties of natural fiber-reinforced composites are profoundly influenced by the bonding between the fibers and the matrix. This study involves the testing of compact tension (CT) specimens under mode I fracture conditions and employs three-dimensional finite element analysis (FEA) using ANSYS software to enhance our understanding of the material’s fracture behavior. Finite element analysis was performed on coconut inflorescence stem fiber-reinforced composite (CIFRC) panels with preformed cracks. Numerical simulation was carried out using ANSYS software. Properties such as crack growth initiation, stress-intensity factor, and stresses along the length of a CIFRC panel were examined using finite element analysis (FEA). ASTM D-5045 standards were followed for the specimen size and the ASTM E399 standard was followed for the finite element pre-cracking. The simulation results were found to be in good agreement with the analytical results. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 1114 KB

Open AccessProceeding Paper

Synthesis, Characterization, and Regeneration of Ag/TiO₂ Nanoparticles: Photocatalytic Removal of Mixed Dye Pollutants

by Zainab S. Mahdi, Aseel M. Aljeboree, Fadhil A. Rasen, Noor Abd Alkhudhur Salman and Ayad F. Alkaim

Eng. Proc. 2023, 59(1), 216; https://doi.org/10.3390/engproc2023059216 - 26 Jan 2024

Cited by 6 | Viewed by 1719

Abstract

Titanium dioxide nanoparticles were prepared via the hydrothermal method, and silver was supported on Ti

O_{2}

nanoparticles to form Ag/Ti

O_{2}

using the photoreduction method. The prepared samples were dried overnight at

60^{\circ} C

and then calcined at [...] Read more.

Titanium dioxide nanoparticles were prepared via the hydrothermal method, and silver was supported on Ti

O_{2}

nanoparticles to form Ag/Ti

O_{2}

using the photoreduction method. The prepared samples were dried overnight at

60^{\circ} C

and then calcined at

500^{\circ} C

for 2 h. Structural and morphological characterization were carried out using X-ray diffraction, field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM). The adsorption performance and photocatalytic activity of the Ag/Ti

O_{2}

were investigated using malachite green dye (MG) as a model organic pollutant in water. Along the way, the effects of various parameters were examined, such as regeneration experiments and removal of a laboratory sample (a mixture of several dyes) from aqueous solutions. The photocatalytic degradation efficiency reached 83.9%, 78.8%, and 68.5% during three cycles, compared to the standard solution (fresh), which reached 90.9%. These results underscore the potential application of Ag/Ti

O_{2}

in environmental remediation, particularly in the degradation of organic dyes. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 3007 KB

Open AccessProceeding Paper

Influence of Multiwalled Carbon Nanotubes in Sulfur/Carbon Nanotube Composites Synthesized Using Solution Casting Method

by Karishma Jain, Sushil Kumar Jain, Anu Malhotra, Shalini Dixit, Balram Tripathi and Rajesh Sahu

Eng. Proc. 2023, 59(1), 217; https://doi.org/10.3390/engproc2023059217 - 25 Jan 2024

Cited by 4 | Viewed by 2010

Abstract

In this manuscript, we are reporting on the influence of MWNTs (multiwalled carbon nanotubes) on the structural, bonding, and surface morphological response on sulfur nanoparticles. Sulfur and multiwalled carbon nanotube (MWCNT) composites are formed using the solution casting method. The concentration of MWCNTs [...] Read more.

In this manuscript, we are reporting on the influence of MWNTs (multiwalled carbon nanotubes) on the structural, bonding, and surface morphological response on sulfur nanoparticles. Sulfur and multiwalled carbon nanotube (MWCNT) composites are formed using the solution casting method. The concentration of MWCNTs (0.01 and 0.05) and sulfur (0.99 and 0.95), respectively, was taken in weight ratios during fabrication of the composites. These fabricated composites have been characterized using XRD (X-ray diffraction), FESEM (field emission scanning electron microscopy), and FTIR (Fourier-transform infrared spectroscopy) techniques. XRD spectra reveal that the crystallite size distribution was in the range of ca. 55 nm to 78 nm, as well as enhanced crystallinity upon increasing the concentration of MWCNTs in sulfur composites. Dislocation density and strain have been found to be increased in composites showing increased augmentation of MWCNTs (i.e., S95% MWCNT5%), while FESEM images confirm the uniform distribution of MWCNTs in sulfur composites, along with round structures at the nanoscale range. FTIR spectra depicted the bending and stretching of C-H bands. Composites with a higher concentration of MWCNTs show slightly more stretching vibrations. This indicates the further delocalization of electrons, which reveals that as MWCNTs’ concentration is increased, electrical conductivity enhances, showing that MWCNTs could perform better in electrical industries. The further delocalization of electrons also expresses that free electron–hole pair formation is better in composites with a higher concentration of MWCNTs, accounting for the fact that the photocatalytic response may increase in composites with a higher concentration of MWCNTs. Overall, it can be said that as the MWCNT concentration is ameliorated, the composites show a more crystallized structure with more vibrations. This characteristic of MWCNTs/sulfur composites is useful in photocatalytic response as well as in cathode materials in sulfur batteries. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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16 pages, 1332 KB

Open AccessProceeding Paper

Multi-Objective Ant Colony Optimization (MOACO) Approach for Multi-Document Text Summarization

by Murali Krishna Muddada, Jayavani Vankara, Sekharamahanti S. Nandini, Girija Rani Karetla and Kaparapu Sowjanya Naidu

Eng. Proc. 2023, 59(1), 218; https://doi.org/10.3390/engproc2023059218 - 27 Jan 2024

Cited by 7 | Viewed by 3060

Abstract

The demand for creating automatic text summarization methods has significantly emerged as a result of the web’s explosive growth in textual data and the challenge of finding re-quired information within this massive volume of data. Multi-document text summarizing (MDTS) is an effective method [...] Read more.

The demand for creating automatic text summarization methods has significantly emerged as a result of the web’s explosive growth in textual data and the challenge of finding re-quired information within this massive volume of data. Multi-document text summarizing (MDTS) is an effective method for creating summaries by grouping texts that are relevant to a similar subject. With the aid of optimization methods, this strategy can be optimized. The majority of optimization algorithms used in the scientific literature are single-objective ones, but more recently, multi-objective optimization (MOO) techniques have been created, and their findings have outperformed those of single-objective methods. Metaheuristics-based techniques are also increasingly being used effectively in the study of MOO. The MDTS issue is therefore solved by the Multi-Objective Ant Colony Optimization (MOACO) method. This multi-objective metaheuristic algorithm is based on the Pareto optimization. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics have been used to assess the outcomes of experiments using Document Understanding Conferences (DUC) datasets. Additionally, they have consistently outperformed other referenced summarizer systems. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 4725 KB

Open AccessProceeding Paper

Enhanced Drug Delivery and Wound Healing with Novel Hydrogel Nanocomposite

by Aseel M. Aljeboree, Ishraq T. Hasan, Maher Mohammed Jwaid, Ashour H. Dawood and Mohammed Abed Jawad

Eng. Proc. 2023, 59(1), 219; https://doi.org/10.3390/engproc2023059219 - 29 Jan 2024

Cited by 1 | Viewed by 1710

Abstract

This study explores the biological activity, drug release properties, and wound healing efficacy of a novel hydrogel nanocomposite, (NaA-g-Poly(ITA-co-NaSS)/CPL). Firstly, the physicochemical properties of the hydrogel nanocomposite surface were characterized using FE-SEM and TEM imaging, demonstrating a solid, layered morphology with many interconnected [...] Read more.

This study explores the biological activity, drug release properties, and wound healing efficacy of a novel hydrogel nanocomposite, (NaA-g-Poly(ITA-co-NaSS)/CPL). Firstly, the physicochemical properties of the hydrogel nanocomposite surface were characterized using FE-SEM and TEM imaging, demonstrating a solid, layered morphology with many interconnected pores. The nitrogen isothermal adsorption technique further supported these observations by indicating an enhanced surface area, pore diameter, and total pore volume following hydrogel incorporation. Secondly, the in vitro release of the drug chlorazepam from the hydrogel nanocomposite was investigated, revealing pH-responsive behavior with an increased release rate at a neutral to slightly alkaline pH (7.5). This is hypothesized to be due to the increased swelling of the hydrogel at this pH, facilitating drug dissolution and release. The study also examined the antimicrobial activity of the hydrogel nanocomposite against Gram-positive and Gram-negative bacteria, as well as a type of fungus, Aspergillus flavus. The hydrogel nanocomposite demonstrated superior antimicrobial activity in comparison to CPL and (NaA-g-Poly(ITA-co-NaSS). Lastly, the hydrogel nanocomposite exhibited enhanced wound healing efficiency in mice models, healing injuries faster and more effectively. In conclusion, this study suggests that the (NaA-g-Poly(ITA-co-NaSS)/ CPL) hydrogel nanocomposite holds significant promise for various biomedical applications due to its robust antimicrobial properties, pH-responsive drug release behavior, and wound healing capabilities. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 4776 KB

Open AccessProceeding Paper

Characterization and Removal Efficiency Analysis of MWCNT/Clay Nanocomposites for MB Dye Adsorption

by Firas H. Abdulrazzak, Aseel M. Aljeboree, Dalya K. Naser, Ashour H. Dawood, Montather F. Ramadan and Ayad F. Alkaim

Eng. Proc. 2023, 59(1), 220; https://doi.org/10.3390/engproc2023059220 - 29 Jan 2024

Cited by 2 | Viewed by 1556

Abstract

Multi-walled carbon nanotubes (MWCNTs) combined with clay have shown potential as effective adsorbents for dye removal. This study aims to characterize MWCNT/clay nanocomposites and analyze their removal efficiency for methylene blue (MB) dye under various conditions. The nanocomposites were characterized using techniques such [...] Read more.

Multi-walled carbon nanotubes (MWCNTs) combined with clay have shown potential as effective adsorbents for dye removal. This study aims to characterize MWCNT/clay nanocomposites and analyze their removal efficiency for methylene blue (MB) dye under various conditions. The nanocomposites were characterized using techniques such as FESEM, TEM, EDX, TGA, and XRD. The removal efficiency was studied concerning different weights, concentrations, temperatures, pH levels, and comparative amounts of CNT in the composites. The findings revealed distinct properties and behaviors of the nanocomposites, with removal efficiency significantly influenced by weight, MB dye concentration, temperature, and pH. A higher CNT content in the composite corresponded to better removal results. The study demonstrates the potential of MWCNT/clay nanocomposites in wastewater treatment, with insights into optimal conditions for dye removal. The investigation adds valuable knowledge to the field and indicates promising directions for future research. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 1898 KB

Open AccessProceeding Paper

Optimizing and Analyzing a Centrifugal Compressor Impeller for 50,000 rpm: Performance Enhancement and Structural Integrity Assessment

by Gayathri Ravinath, Ponmary Pushpa Latha, Lakshmi Priya and Joshna Ramesh

Eng. Proc. 2023, 59(1), 221; https://doi.org/10.3390/engproc2023059221 - 29 Jan 2024

Cited by 4 | Viewed by 4177

Abstract

The centrifugal compressor impeller is a critical component in enhancing the energy of working fluid, endowing the compressor with its characteristic centrifugal nature. This study focuses on optimizing a radial impeller in a shark-like configuration, specifically a dorsal fin shape, without modifying the [...] Read more.

The centrifugal compressor impeller is a critical component in enhancing the energy of working fluid, endowing the compressor with its characteristic centrifugal nature. This study focuses on optimizing a radial impeller in a shark-like configuration, specifically a dorsal fin shape, without modifying the remaining blades, aiming to improve performance and assess the impeller’s structural integrity at high rotational speeds of 50,000 rpm. The enhanced design is anticipated to increase the efficiency of the combustor significantly. Utilizing Solid Works 2019 for modeling and ANSYS for Computational Fluid Dynamics (CFD) simulations, this study examines the overall deformation and von Mises stress experienced by the impeller. Structural analyses are performed on two distinct materials: aluminum alloy 2618 and Ti 6-2-4-6 alloy, to determine the optimal material choice for various applications. The findings delineate crucial design parameters and material selection criteria that could lead to substantial advancements in compressor technology. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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11 pages, 4125 KB

Open AccessProceeding Paper

Synthesis of Fused Isoxazoles: A Comprehensive Review

by Niveditha N. Mallik, Chandramouli Manasa, Vrushabendra Basavanna, Dileep C. Shanthakumar, Srikantamurthy Ningaiah and Nagarakere S. Lingegowda

Eng. Proc. 2023, 59(1), 222; https://doi.org/10.3390/engproc2023059222 - 30 Jan 2024

Cited by 14 | Viewed by 5549

Abstract

Pharmaceutically important isoxazoles are within the wide range of heterocycles. The isoxazole ring, being five-membered, is also found in many bioactive natural products in addition to synthetic drugs. Many significant properties are exhibited by synthetically modified isoxazoles. Fused isoxazoles have widely shown their [...] Read more.

Pharmaceutically important isoxazoles are within the wide range of heterocycles. The isoxazole ring, being five-membered, is also found in many bioactive natural products in addition to synthetic drugs. Many significant properties are exhibited by synthetically modified isoxazoles. Fused isoxazoles have widely shown their therapeutic potential as anticancer, insecticidal, antibacterial, antituberculosis, antifungal, antibiotic, antitumor, and antiulcerogenic agents. A variety of strategies are employed for the synthesis of these compounds, which are known for their pharmacological importance. Their synthesis is here reviewed. Synthesized isoxazoles have appeared as good forerunners for many other different molecules. This review summarizes the various synthesis approaches described so far for isoxazoles, providing a detailed study of their synthesis process. Substituted isoxazoles have been well discussed in the literature for their significant biological activities. This review is mainly focused on the synthesis of fused isoxazoles. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 5617 KB

Open AccessProceeding Paper

Blockchain Technology for Sustainable Management of Electricity and Water Consumption

by Muath Alrammal, Fadi Abu-Amara, Zamhar Ismail and Muhammad Nadeem

Eng. Proc. 2023, 59(1), 223; https://doi.org/10.3390/engproc2023059223 - 31 Jan 2024

Cited by 5 | Viewed by 2563

Abstract

Electricity and water are vital resources, but the current management systems face challenges due to growing demand and regulatory complexities. To address this, we introduce a novel blockchain-based solution for managing electricity and water services. Our proposed system connects various entities through smart [...] Read more.

Electricity and water are vital resources, but the current management systems face challenges due to growing demand and regulatory complexities. To address this, we introduce a novel blockchain-based solution for managing electricity and water services. Our proposed system connects various entities through smart contracts on the blockchain, automates processes, and ensures transparency. Customers can easily view and pay bills, track consumption, and enjoy secure online transactions while preserving their privacy. The shared digital ledger enhances trust among entities and promotes transparency. Additionally, our system contributes to environmental sustainability by reducing paper usage, incentivizing energy-saving devices, and efficiently managing electricity and water consumption. Finally, a meta-analysis of the related work is conducted to highlight the importance of our solution. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 2626 KB

Open AccessProceeding Paper

Exploring the Potential of Copper Slag and Quartz as Fine Aggregate Replacements in Concrete: A Comprehensive Study

by Arunkumar Yadav, Shivakumara Marilingannara Jayappa, Raveesh Ranganathapura Mahadevaiah, Sanjay Gowda, Neelakant Jitesh, Jayachandra Pasmanabh, Vishal Anand and Muralidhar Nagarajaiah

Eng. Proc. 2023, 59(1), 224; https://doi.org/10.3390/engproc2023059224 - 31 Jan 2024

Cited by 5 | Viewed by 2688

Abstract

In the realm of civil construction, river sand is an essential ingredient that cannot be overlooked. With the ever-increasing surge in construction activities, the demand for river sand has surged in tandem, resulting in its escalating scarcity, and subsequently, its price surge across [...] Read more.

In the realm of civil construction, river sand is an essential ingredient that cannot be overlooked. With the ever-increasing surge in construction activities, the demand for river sand has surged in tandem, resulting in its escalating scarcity, and subsequently, its price surge across the entire nation. This study delves into the utilization of copper slag as a viable alternative in the production of cement mortars, particularly as a partial replacement for fine aggregates. Experiments were conducted on concrete cubes and cylinders to determine the compressive strength and split tensile strength, respectively. Five cubes and cylinders were tested after 7, 14, and 21 days of curing. The extensive characterization of copper slag was conducted, encompassing its chemical composition, mineralogical attributes, and size distribution. The findings highlight that mortars containing copper slag exhibit superior compression resistance compared to the river sand-based mortars. Specifically, the 50% replacement of river sand with a blend of copper slag and quartz demonstrates the highest strength, surpassing the other compositions. Notably, the partial substitution of sand with copper slag outperforms both quartz and sand individually, with the optimal strength achieved at a 50% replacement rate. Copper slag, with its pozzolanic properties, showed a greater strength-enhancing potential, while quartz also exhibited positive effects. These findings are promising for optimizing concrete mix designs, reducing the environmental impacts caused by industrial by-products, and exploring natural alternatives. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 607 KB

Open AccessProceeding Paper

Image Fusion Techniques Based on Optimization Algorithms: A Review

by Anamika Goel, Javed Wasim, Prabhat Kumar Srivastava, Kanika Malik and Monika Singh

Eng. Proc. 2023, 59(1), 225; https://doi.org/10.3390/engproc2023059225 - 26 Jan 2024

Cited by 2 | Viewed by 2714

Abstract

In image processing applications, image fusion techniques gain popularity because they combine the most appropriate features of different source images in order to generate a single image that contains more information and is more beneficial. In this paper, initially, we have analysed the [...] Read more.

In image processing applications, image fusion techniques gain popularity because they combine the most appropriate features of different source images in order to generate a single image that contains more information and is more beneficial. In this paper, initially, we have analysed the conventional spatial and transform domain image fusion techniques. These techniques face numerous challenges, such as low contrast, noise, and redundancy. To overcome these challenges, adaptive image fusion methods using nature-inspired optimization algorithms (YSGA) are deployed. These algorithms search for the optimal solution for the image fusion technique based on the objective function. Therefore, the main focus of this paper is to study and analyse the optimization algorithms based on various factors. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 975 KB

Open AccessProceeding Paper

A Mini-Review on Graphene: Exploration of Synthesis Methods and Multifaceted Properties

by Salam Hussein Alwan, Alaa A. Omran, Dalya K. Naser and Montather F. Ramadan

Eng. Proc. 2023, 59(1), 226; https://doi.org/10.3390/engproc2023059226 - 5 Feb 2024

Cited by 16 | Viewed by 6184

Abstract

Graphene, a single layer of carbon atoms arranged in a two-dimensional lattice, has emerged as a material of immense scientific and technological interest. This review article provides a comprehensive overview of the various synthesis techniques for graphene, including chemical vapor deposition (CVD), epitaxial [...] Read more.

Graphene, a single layer of carbon atoms arranged in a two-dimensional lattice, has emerged as a material of immense scientific and technological interest. This review article provides a comprehensive overview of the various synthesis techniques for graphene, including chemical vapor deposition (CVD), epitaxial growth on SiC, mechanical cleavage, and exfoliation of graphite oxide. The article further delves into the distinctive electronic, mechanical, optical, and thermal properties of graphene that make it a promising material for numerous applications. From high electrical conductivity to remarkable strength and unique optical characteristics, graphene’s attributes are explored in detail. The thermal stability of graphene, its interaction with different substrates, and potential applications in electronic devices are also discussed. The review concludes with a summary of the current state of research and prospects for future exploration, emphasizing graphene’s potential to revolutionize various industrial sectors. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1176 KB

Open AccessProceeding Paper

Analytical and Biological Evaluation of Chromium Complex with Organic Detector NTADBrP: Stability, Calibration, and Inhibition Studies

by Mustafa Subhi Fattah and Shaimaa Mohsen Essa

Eng. Proc. 2023, 59(1), 227; https://doi.org/10.3390/engproc2023059227 - 5 Feb 2024

Viewed by 1534

Abstract

This study provides a comprehensive investigation into the formation, characteristics, and biological activity of a chromium complex with the organic reagent NTADBrP. The article presents detailed insights into the H1-NMR spectrum of the organic reagent and examines the optimal conditions for chromium complex [...] Read more.

This study provides a comprehensive investigation into the formation, characteristics, and biological activity of a chromium complex with the organic reagent NTADBrP. The article presents detailed insights into the H1-NMR spectrum of the organic reagent and examines the optimal conditions for chromium complex formation, considering the effects of time, pH, and temperature. The stoichiometry and stability constant of the complex are determined using specific methods, leading to the calculation of a significant stability constant. Additionally, a calibration curve for the chromium ion is derived, and the complex’s biological activity against Escherichia coli and Staphylococcus bacteria is studied. These findings contribute to the understanding of chromium complex behavior and open new avenues for applications in analytical chemistry and pharmaceutical research. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 657 KB

Open AccessProceeding Paper

Simulation and Experimental Analysis of L-Section in Reinforced Cement Concrete: Uncertainties in Performance and Strength

by Balakrishna Srinivas Maddodi, Nithesh Naik, Prasanna Kumar Maddikeri, Shivani Chougule, Shahid Malik Abdu, Dhanaraj Bharathi Narasimha, Ankit Kumar Dubey and Sonal Devesh

Eng. Proc. 2023, 59(1), 228; https://doi.org/10.3390/engproc2023059228 - 7 Feb 2024

Viewed by 1519

Abstract

The design and construction of reinforced cement concrete (RCC) flooring play a crucial role in the overall stability of a structure, particularly in regions prone to tectonic activity. RCC floors comprise various beams, including intermediate T-sections and specific L-sections at critical points such [...] Read more.

The design and construction of reinforced cement concrete (RCC) flooring play a crucial role in the overall stability of a structure, particularly in regions prone to tectonic activity. RCC floors comprise various beams, including intermediate T-sections and specific L-sections at critical points such as corners and around staircases or lift openings. This paper identifies a key challenge in building frameworks to resist tectonic loads. It further explores the components of the structure that provide potential for interruption, capability, and the safe transfer of tectonic loading to the array connection, all while maintaining sufficient strength. The L-sections were experimented on using various grades of concrete and sizes to reinforce connections under diverse loading conditions. L-sections contribute to reducing floor height, solving economic and technical problems, and creating advanced composite connections that integrate the proposed structural system. The analysis was conducted both analytically and experimentally to assess methods to resist earthquake forces based on stiffness, building strength, and elasticity capacity. These approaches have been identified to safeguard buildings during substantial seismic events. The development of the L-section is detailed, highlighting the loading process and the capacity to overcome various structural challenges. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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8 pages, 2820 KB

Open AccessProceeding Paper

Study of Some Thorium Isotopes near to the Closed Shell (82 and 126)

by Nibras Hayder Hammood Eatiah, Mohsin Kadhim Muttaleb Al-Jnaby and Ghaidaa A. Hafedh Jaber Hussien

Eng. Proc. 2023, 59(1), 229; https://doi.org/10.3390/engproc2023059229 - 7 Feb 2024

Viewed by 1174

Abstract

Using the interacting bosons model-one (IBM-1), the nuclear structure of the thorium isotopes ²²⁴Th, ²²⁶Th, and ²²⁸Th were examined in this study, which are near from closed shell 82 and 126. By acquiring this element’s energy levels and comparing them [...] Read more.

Using the interacting bosons model-one (IBM-1), the nuclear structure of the thorium isotopes ²²⁴Th, ²²⁶Th, and ²²⁸Th were examined in this study, which are near from closed shell 82 and 126. By acquiring this element’s energy levels and comparing them to actual values, which provide an indication of these isotopes membership in a specific determination, it is possible to determine that ^224Th and ²²⁶Th belong to transition region between SU(3) and O(6) but ²²⁸Th belong to the SU(3) limit. The ratio of the fourth to the second energy level

E_{4}^{+} / E_{2}^{+}

with other ratios

E_{6}^{+} / E_{2}^{+}

and

E_{8}^{+} / E_{2}^{+}

, the order of practical levels, are first exam to determine the limit that belong. Using IBM program to find theoretical energy level and compared with practical one, also the agreement between the theoretical probability of electric transitions B(E2) through IBMT program was investigated. The IBMP program was used to study the surface potential of the nucleus, which provides insight into the deformation that occurs in the nucleus and from the contour lines deviation. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1223 KB

Open AccessProceeding Paper

Federated Learning for Healthcare: A Comprehensive Review

by Pallavi Dhade and Prajakta Shirke

Eng. Proc. 2023, 59(1), 230; https://doi.org/10.3390/engproc2023059230 - 9 Feb 2024

Cited by 62 | Viewed by 23901

Abstract

Recent advancements in deep learning for healthcare and computer-aided laboratory services have sparked a renewed interest in making medical data more accessible. Elevating the quality of healthcare services and delivering improved patient care necessitates a knowledge base rooted in data-driven insights. Deep learning [...] Read more.

Recent advancements in deep learning for healthcare and computer-aided laboratory services have sparked a renewed interest in making medical data more accessible. Elevating the quality of healthcare services and delivering improved patient care necessitates a knowledge base rooted in data-driven insights. Deep learning models have proven to excel in this regard, as they are specifically designed to embrace a data-driven approach. These models thrive on exposure to larger datasets, which enables them to continuously improve their performance. However, as healthcare organizations strive to aggregate clinical records onto central servers to construct robust deep learning models, concerns surrounding privacy, data ownership, and legal restrictions have emerged. Safeguarding sensitive medical data while harnessing collective knowledge from multiple healthcare centers is a challenging balancing act. One promising approach to address these concerns is the use of privacy-preserving techniques that allow for the utilization of data from multiple centers without compromising security. Federated learning (FL) is a technique that has emerged to enable the deployment of large machine learning models trained across multiple data centers without the necessity of sharing sensitive information. In this article, we present the most recent findings derived from a systematic literature review focusing on the application of federated learning in healthcare settings. This review offers insights into the current state of research and practical implementations of FL within the healthcare domain. By leveraging federated learning, healthcare institutions can harness the collective power of their data while upholding privacy and data security standards, ultimately leading to more effective and data-driven healthcare solutions. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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13 pages, 594 KB

Open AccessProceeding Paper

A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime

by Abhay R. Gaidhani and Amol D. Potgantwar

Eng. Proc. 2023, 59(1), 231; https://doi.org/10.3390/engproc2023059231 - 20 Feb 2024

Cited by 26 | Viewed by 4683

Abstract

Wireless sensor networks (WSNs) grapple with a challenging and pivotal issue: how to maximize the network’s lifespan. To improve the quality of service (QoS) and extend the life of the network, there has been a lot of effort made in this area in [...] Read more.

Wireless sensor networks (WSNs) grapple with a challenging and pivotal issue: how to maximize the network’s lifespan. To improve the quality of service (QoS) and extend the life of the network, there has been a lot of effort made in this area in recent years. The sensor nodes of a WSN are autonomous, dispersed devices that gather and direct data to a central hub, or “Base Station”, using wireless connections without any central coordinator. These networks have less processing power, memory capacity, power supply, and so on, which limits their range and battery life. Numerous studies preceding our research have proposed a myriad of strategies to enhance network longevity. In a WSN, information is relayed from one node to the next until it reaches the base station. Most nodes may be reliably expected to operate for the duration of their batteries. These strategies encompass reducing energy consumption, minimizing latency, load balancing, clustering, efficient data aggregation, and curtailing data transmission delays. WSNs may alter dynamically as a result of internal or external circumstances, necessitating a depreciating dispensable redesign of the network. Because the networks in classic WSN techniques are expressly programmed, it is difficult for them to respond dynamically. Machine learning (ML) approaches can be used to respond appropriately in order to overcome such circumstances. Machine learning is the process of acting without human involvement or reprogramming in order to learn from experiences. This paper presents a review of different ML-based algorithms for WSNs together with their benefits, limitations, and parameters that affect network longevity. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 5066 KB

Open AccessProceeding Paper

Transport Vehicle Demand Prediction Using Context-Aware Neural Networks

by Pankaj Kunekar, Kunal Jadhav, Amrut Bhagwat, Aditya Kirar, Ankit Singh, Sonal Devesh and Ritesh Bhat

Eng. Proc. 2023, 59(1), 232; https://doi.org/10.3390/engproc2023059232 - 21 Feb 2024

Cited by 1 | Viewed by 1547

Abstract

Transport is an important aspect of trade. The more efficient the transport system, the more trade will flourish. However, sometimes it is the case that vehicles are not available for transport. This necessitates a system which could be able to keep an eye [...] Read more.

Transport is an important aspect of trade. The more efficient the transport system, the more trade will flourish. However, sometimes it is the case that vehicles are not available for transport. This necessitates a system which could be able to keep an eye on the demand of transport vehicles. If the demand is fulfilled properly, then trade will flourish in a much better way. Thus, this project aims to keep an eye on the demand of transport vehicles and fulfill it. The study used MLP and LSTM models to work. The project also shows a comparison between the gradual changes and improvements in MLP and LSTM and the type of data used. The study focus was to predict the demand accurately in an area. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 6542 KB

Open AccessProceeding Paper

A Numerical Study on Coconut Inflorescence Stem-Fiber-Reinforced Panels Subjected to Tensile Load, Compressive Load, and Flexural Load

by Muralidhar Nagarajaiah, Raveesh Ranganathappa Mahadevaiah, Kishan Rao Bangri Parshuram and Pavan Hiremath

Eng. Proc. 2023, 59(1), 233; https://doi.org/10.3390/engproc2023059233 - 22 Feb 2024

Cited by 1 | Viewed by 1839

Abstract

Natural-fiber-reinforced composites are attracting an increasing amount of interest, and they are becoming more popular as a replacement for synthetic-fiber-reinforced composites. Natural-fiber-reinforced composites are important as a potential building material due to their lightweight nature, strength, and favorable qualities, which include eco-friendliness, non-toxicity, [...] Read more.

Natural-fiber-reinforced composites are attracting an increasing amount of interest, and they are becoming more popular as a replacement for synthetic-fiber-reinforced composites. Natural-fiber-reinforced composites are important as a potential building material due to their lightweight nature, strength, and favorable qualities, which include eco-friendliness, non-toxicity, and biodegradability. Natural fibers such as hemp fibers, jute fibers, banana fibers, coconut fibers, sisal fibers, bamboo fibers, areca nut fibers, and kenaf fibers have been used for making composite panels because of their strength-to-weight ratio. Coconut inflorescence stem fibers are considered for our study. Coconut inflorescence stem-reinforced composite panels are often subjected to tensile load, compression load, and flexural load. Tensile strength, compressive strength, and flexural strength play a vital role when these panels are subjected to service loads. In this context, finite element analysis (FEA) is carried out on coconut inflorescence stem-reinforced panels subjected to tensile load, compressive load, and flexural load. A linear analysis is performed for the mechanical properties by using ANSYS workbench 2021 R1. A coconut inflorescence stem-reinforced composite specimen with the dimensions 280 mm × 25 mm × 3 mm (length × width × thickness) for tensile loading, 145 mm × 25 mm × 4 mm for the compressive load, and 150 mm × 25 mm × 4 mm for the flexural load is considered for the present study, as per the ASTM-D3039, ASTM-D3410, and ASTM-D790 standards, respectively. Finite element analysis results showed good correlation with the analytical results. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 743 KB

Open AccessProceeding Paper

Review and Analysis of the Literature: Artificial Intelligence-Based Digital Transformation of Automated Customer Onboarding

by Vijay Thokal and Purushottam R. Patil

Eng. Proc. 2023, 59(1), 234; https://doi.org/10.3390/engproc2023059234 - 11 Jan 2024

Cited by 7 | Viewed by 6140

Abstract

Digital transformation in customer onboarding represents a paradigmatic shift in the way businesses engage with their clients. This process harnesses the power of digital technologies to create a seamless and highly efficient onboarding experience. The key objectives of digital client onboarding include saving [...] Read more.

Digital transformation in customer onboarding represents a paradigmatic shift in the way businesses engage with their clients. This process harnesses the power of digital technologies to create a seamless and highly efficient onboarding experience. The key objectives of digital client onboarding include saving time and effort, achieving cost savings and operational optimization, enhancing the overall customer experience, and ultimately increasing revenue. In the context of digital transformation customer onboarding, a wide array of digital tools and platforms are employed to facilitate the collection and processing of customer information. This enables the automation of previously manual procedures and allows businesses to offer personalized support throughout the onboarding journey. Compared to traditional customer onboarding methods, digital client onboarding offers several distinct advantages. Firstly, it saves valuable time and effort for both businesses and clients. The automation of various tasks, such as data entry and document verification, streamlines the onboarding process, allowing clients to quickly access the products or services they seek. This efficiency also translates into significant cost savings as businesses reduce overheads associated with manual processes, such as paperwork and administrative tasks. Furthermore, digital transformation in customer onboarding leads to a substantial improvement in the overall customer experience. Clients benefit from a faster and more convenient onboarding process, reducing the likelihood of frustration or abandonment. This enhanced experience fosters customer satisfaction and loyalty, ultimately contributing to increased revenue through repeat business and referrals. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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6 pages, 2367 KB

Open AccessProceeding Paper

Track-Me-Down Emergency Location Service Provider

by Harsh Bodkhe, Chinmay Bilade, Dimple Naik, Onkar Deshmukh, Aatmaja Bulakh, Prathamesh Potdar, Ketki Shirbavikar and Sachin Komble

Eng. Proc. 2023, 59(1), 235; https://doi.org/10.3390/engproc2023059235 - 26 Feb 2024

Viewed by 3355

Abstract

Object tracking and detection are fundamental and challenging tasks in various computer vision applications, spanning surveillance, vehicle navigation, and autonomous robot control. These tasks are particularly critical in the context of video monitoring within dynamic environments, where the detection and tracking of objects, [...] Read more.

Object tracking and detection are fundamental and challenging tasks in various computer vision applications, spanning surveillance, vehicle navigation, and autonomous robot control. These tasks are particularly critical in the context of video monitoring within dynamic environments, where the detection and tracking of objects, such as people and automobiles, play a pivotal role. In today’s world, as we combat crime and terrorism, ensure public safety, and manage traffic effectively, advanced computer vision technology has become indispensable. Video monitoring in dynamic environments is at the forefront of this battle, providing crucial insights and real-time information for decision making. Object-tracking-based techniques emerge as a strong choice, especially for detecting stationary foreground objects. These methods exhibit robust performances when the camera remains stationary, even in scenarios in which the ambient lighting conditions gradually change. This stability makes them well suited for applications requiring consistent and reliable object detection. In the contemporary landscape, one of the most pressing concerns revolves around the recognition of objects and the real-time tracking of their locations. Achieving these objectives is paramount for enhancing security, safety, and efficiency across various domains. However, it is essential to acknowledge that, in some scenarios, such as remote or isolated locations with limited Internet connectivity, access to advanced object-tracking and detection technologies may be constrained. Therefore, addressing these challenges and developing robust, offline-capable solutions remains a critical area of research and development in computer vision. In conclusion, object tracking and detection are pivotal technologies in computer vision, with applications spanning from surveillance to traffic management. In dynamic environments, they play a crucial role in enhancing security and safety. However, addressing the challenges related to real-time tracking and detection in resource-constrained settings is an ongoing research endeavor. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1055 KB

Open AccessProceeding Paper

Effect of Violet Laser Irradiation on the Optical Properties of Polyvinyl Alcohol/Methyl Orange Composite Thick Films: A Model for Medical Applications

by Sarah Maysam Tareq, Nihal A. AbdulWahhab and Addnan H. Al-araji

Eng. Proc. 2023, 59(1), 236; https://doi.org/10.3390/engproc2023059236 - 27 Feb 2024

Cited by 3 | Viewed by 1402

Abstract

This study investigates the impact of violet laser irradiation on the optical properties of thick films composed of polyvinyl alcohol (PVA), methyl orange (MO), and their composite (PVA/MO). Aimed at exploring potential medical applications, the films were synthesized through a casting process involving [...] Read more.

This study investigates the impact of violet laser irradiation on the optical properties of thick films composed of polyvinyl alcohol (PVA), methyl orange (MO), and their composite (PVA/MO). Aimed at exploring potential medical applications, the films were synthesized through a casting process involving the dissolution of PVA and MO in distilled water. The optical properties, including absorbance spectra, energy gaps, and various optical constants, were meticulously measured before and after exposure to laser irradiation. The results revealed a notable decrease in the absorbance spectra and optical constants, along with an increase in the energy gaps, suggesting a structural modification induced by the laser treatment. These findings hold significance for the advancement of materials with customized optical features, potentially serving as a model for future developments in optoelectronic and photovoltaic devices. The research outcomes provide a foundation for the exploration of polymers and dyes in medical applications, particularly in the realms of non-invasive surgical procedures and simulations. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 6201 KB

Open AccessProceeding Paper

Spectral Properties and Biological Activities of Binuclear Mixed-Metal Bridged Thiocyanate Complexes Containing Schiff Bases Derived from Isatin

by Adyan Hameed Jasim, Mouayed Yousif Kadhum and Sanaa Qasem Badr

Eng. Proc. 2023, 59(1), 237; https://doi.org/10.3390/engproc2023059237 - 4 Mar 2024

Cited by 1 | Viewed by 1660

Abstract

This study introduces two novel Schiff base ligands synthesized through the condensation of isatin with primary amines. These ligands were characterized using infrared (IR), ultraviolet-visible (UV-Vis) spectroscopy, mass spectrometry, and elemental analysis. The ligands were further treated with tetrathiocyanate in a 1:1 molar [...] Read more.

This study introduces two novel Schiff base ligands synthesized through the condensation of isatin with primary amines. These ligands were characterized using infrared (IR), ultraviolet-visible (UV-Vis) spectroscopy, mass spectrometry, and elemental analysis. The ligands were further treated with tetrathiocyanate in a 1:1 molar ratio to yield binuclear mixed-metal monomeric bridged complexes of the form LMCd(SCN)₄, where M represents Co(II) or Ni(II). Various spectral techniques and magnetic susceptibility measurements were employed for the identification of these complexes. The findings revealed that all the complexes possessed a coordination number of four and exhibited non-electrolytic behavior. Additionally, the cobalt complexes demonstrated paramagnetic properties, whereas the nickel complexes were diamagnetic. The antimicrobial efficacy of the Schiff base ligands and their complexes was also evaluated against selected microorganisms, revealing significant antimicrobial activities. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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16 pages, 1333 KB

Open AccessProceeding Paper

Metaheuristic Algorithms for Optimization: A Brief Review

by Vinita Tomar, Mamta Bansal and Pooja Singh

Eng. Proc. 2023, 59(1), 238; https://doi.org/10.3390/engproc2023059238 - 13 Mar 2024

Cited by 188 | Viewed by 36305

Abstract

In the area of optimization, metaheuristic algorithms have attracted a lot of interest. For many centuries, human beings have utilized metaheuristic algorithms as a problem-solving approach. The application of these methods to combinatorial optimization problems has rapidly become a growing area of research, [...] Read more.

In the area of optimization, metaheuristic algorithms have attracted a lot of interest. For many centuries, human beings have utilized metaheuristic algorithms as a problem-solving approach. The application of these methods to combinatorial optimization problems has rapidly become a growing area of research, incorporating principles of natural selection, evolution, and problem-solving strategies. While conventional software engineering methods may not always be effective in resolving software issues, mathematical optimization using metaheuristics can offer a solution. As a result, metaheuristics have become an increasingly important part of modern optimization, with a large number of algorithms emerging over the last two decades. The purpose of this study is to present a quick overview of these algorithms so that researchers may choose and use the best metaheuristic method for their optimization issues. The key components and concepts of each type of algorithm have been discussed, highlighting their benefits and limitations. This paper aims to provide a comprehensive review of these algorithms, including evolution-based methods, swarm intelligence-based, physics-based, human-related, and hybrid metaheuristics by highlighting their key components and concepts and comparing and contrasting their similarities and differences. This work also addressed some of the difficulties associated with metaheuristic algorithms. Some practical uses of these metaheuristic algorithms were addressed. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 762 KB

Open AccessProceeding Paper

Machine Learning-Based Weld Classification for Quality Monitoring

by Rojan Ghimire and Rajiv Selvam

Eng. Proc. 2023, 59(1), 241; https://doi.org/10.3390/engproc2023059241 - 15 Mar 2024

Cited by 10 | Viewed by 3614

Abstract

The welding industry plays a fundamental role in manufacturing. Ensuringweld quality is critical when safety, reliability, performance, and the associated cost are taken into account. A ungsten inert gas (TIG) weld quality assessment can be a laborious and time-consuming process. The current state [...] Read more.

The welding industry plays a fundamental role in manufacturing. Ensuringweld quality is critical when safety, reliability, performance, and the associated cost are taken into account. A ungsten inert gas (TIG) weld quality assessment can be a laborious and time-consuming process. The current state of the art is quite simple, with a person continuously monitoring the procedure. However, this approach has some limitations. Operator decisions can be subjective, and fatigue can affect their observations, leading to inaccuracies in the assessment. In this research project, a deep learning approach is proposed to classify weld defects using convolutional neural networks (CNNs) to automate the process. The dataset used for this project is sourced from Kaggle, provided by Bacioiu et al. The proposed CNN-based approach aims to accurately classify weld defects using the image data. This study trains the model on the welding dataset, using five convolutional layers followed by five pooling layers and, finally, three fully connected layers. The softmax activation function is employed in the output layer to categorize the input into the six weld categories. The per-class metrics, such as precision, recall, and F1-score, suggest that the model is dependable and accurate. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 414 KB

Open AccessProceeding Paper

Exploring Flexural Strength in High-Performance Concrete with Iron Slag and Copper Slag as Sand Substitutes

by Ravindranatha, Ashwin Shenoy and Sidharth

Eng. Proc. 2023, 59(1), 242; https://doi.org/10.3390/engproc2023059242 - 20 Mar 2024

Cited by 4 | Viewed by 2041

Abstract

The primary objective of this study is to explore the potential of iron slag and copper slag as alternatives to traditional sand in concrete, with an emphasis on assessing the impact on flexural strength. This investigation experimented with three distinct mixtures: a combination [...] Read more.

The primary objective of this study is to explore the potential of iron slag and copper slag as alternatives to traditional sand in concrete, with an emphasis on assessing the impact on flexural strength. This investigation experimented with three distinct mixtures: a combination of cement with iron slag and copper slag, a blend of cement with sand and iron slag, and a mixture of cement with sand and copper slag. The study varied the proportions of the combinations and the duration of curing, which included intervals of 7, 28, 56, and 90 days, to serve as independent variables. The experimental results suggested that a mixture of iron slag and copper slag in a ratio of 40:60, in conjunction with cement, yielded the most promising results, with an enhancement in flexural strength of up to 92% observed over the 90-day curing period compared to the initial 7-day strength measurement. The findings from this research offer valuable insights into the utilization of waste materials in the construction industry, addressing crucial concerns related to environmental sustainability and solid waste management. The implications of this study extend beyond mere technical outcomes, emphasizing the need for innovative approaches in the construction sector that contribute to ecological conservation and waste reduction. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 489 KB

Open AccessProceeding Paper

Optimization of Turning Parameters and Cooling Techniques for Enhanced Machining Performance of EN8 Steel Using L9 Orthogonal Array

by Barkur Shrinivasa Somayaji, Ritesh Bhat, Nithesh Naik and Beedu Rajendra

Eng. Proc. 2023, 59(1), 243; https://doi.org/10.3390/engproc2023059243 - 20 Mar 2024

Cited by 3 | Viewed by 2100

Abstract

This study presents a detailed analysis of the effects of machining parameters, including the cutting speed (v), feed (f), depth of cut (d), and type of coolant flow (CF), on two primary performance characteristics in a machining process, namely, surface roughness (Ra) and [...] Read more.

This study presents a detailed analysis of the effects of machining parameters, including the cutting speed (v), feed (f), depth of cut (d), and type of coolant flow (CF), on two primary performance characteristics in a machining process, namely, surface roughness (Ra) and material removal rate (MRR). A series of experiments were conducted, and the resulting data were analyzed using regression models, analysis of variance (ANOVA), Taguchi’s L9 orthogonal array analysis, and grey relational analysis. The initial findings from the raw experimental data revealed that, while Ra appeared to be influenced by a combination of parameters, an increasing trend in MRR was observed with higher values of feed rate and depth of cut. The regression models suggested the significant influence of the machining parameters on the Ra and MRR, with the type of coolant flow playing a critical baseline role. The ANOVA results statistically validated these models and ranked the significance of each parameter in affecting Ra and MRR. Furthermore, Taguchi’s analysis supported the findings and highlighted the potential for process optimization. The grey relational analysis revealed that the combination with a speed of 130 m/min, a feed of 0.1 mm/rev, a depth of cut of 0.15 mm, and a minimum quantity lubrication type of coolant flow provided the optimal result, with a GRG of 0.704, ranking first among all other parameter combinations, providing valuable insights for improving machining processes. The results, thus, indicated that the best results were generally obtained with higher speeds, lower feed rates, and moderate depths of cut under minimal quantity lubrication conditions. These findings could greatly benefit industry professionals in optimizing their processes for efficiency and quality, though it is noted that results may vary with different materials and machining conditions, presenting potential areas for future research. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 3034 KB

Open AccessProceeding Paper

Spectrophotometric Determination of Amaranth Dye Using a Two-Step Green Cloud Point and Magnetic Solid-Phase Extraction Approach

by Remah A. Hassan and Zianab Tariq

Eng. Proc. 2023, 59(1), 244; https://doi.org/10.3390/engproc2023059244 - 4 Apr 2024

Cited by 4 | Viewed by 2438

Abstract

The present study introduces a two-step extraction methodology that integrates cloud point extraction (CPE) with magnetic solid-phase extraction (MSPE) for the extraction and quantification of amaranth dye. Initially, the dye is extracted using CPE in the micellar phase of the non-ionic surfactant Triton [...] Read more.

The present study introduces a two-step extraction methodology that integrates cloud point extraction (CPE) with magnetic solid-phase extraction (MSPE) for the extraction and quantification of amaranth dye. Initially, the dye is extracted using CPE in the micellar phase of the non-ionic surfactant Triton X-114. Subsequently, hydrophobic tetraethyl orthosilicate (TEOS)-modified Fe

_{3}

O

_{4}

magnetic nanoparticles (MNPs) are employed to recover the micellar phase. A comprehensive evaluation was conducted to optimize the key parameters influencing the efficacy of both CPE and MSPE techniques, as well as signal enhancement. Under optimized conditions, the proposed methodology exhibited a linear response in the concentration range of 10 to 90

μ

g Kg

^{- 1}

, with a correlation coefficient (

R^{2}

) of 0.9945. The detection limit was determined to be 8.443

μ

g g

^{- 1}

. This robust and environmentally friendly approach offers a promising avenue for the accurate and efficient determination of amaranth dye in various applications. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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12 pages, 3348 KB

Open AccessProceeding Paper

Evaluation of Combined Effect of Zero Flux and Convective Boundary Conditions on Magnetohydrodynamic Boundary-Layer Flow of Nanofluid over Moving Surface Using Buongiorno’s Model

by Purnima Rai and Upendra Mishra

Eng. Proc. 2023, 59(1), 245; https://doi.org/10.3390/engproc2023059245 - 10 Apr 2024

Cited by 4 | Viewed by 1651

Abstract

This study explores the synergistic impact of zero flux and convective boundary conditions on the magnetohydrodynamic (MHD) boundary-layer slip flow of nanofluid over a moving surface, utilizing Buongiorno’s model. In a landscape of expanding nanofluid applications, understanding boundary condition interactions is crucial. Employing [...] Read more.

This study explores the synergistic impact of zero flux and convective boundary conditions on the magnetohydrodynamic (MHD) boundary-layer slip flow of nanofluid over a moving surface, utilizing Buongiorno’s model. In a landscape of expanding nanofluid applications, understanding boundary condition interactions is crucial. Employing a systematic approach, we varied key parameters, including surface velocity, thermophoresis, Brownian motion, Eckert number, Prandtl number, and Lewis number, systematically investigating their effects on flow and heat transfer. Numerical simulations focused on critical metrics such as skin friction coefficients; Nusselt and Sherwood numbers; and temperature, concentration, and velocity profiles. Noteworthy findings include the amplifying effect of a magnetic field and viscous dissipation on temperature profiles and the dual impact of heightened velocity slip on temperature and velocity profiles, which result in a thicker concentration boundary layer. Beyond academia, we envision our research having practical applications in optimizing high-temperature processes, bio-sensors, paints, pharmaceuticals, coatings, cosmetics, and space technology. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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10 pages, 1608 KB

Open AccessProceeding Paper

Tomographic Evaluation of the Efficacy of Three Rotary Retreatment Systems for Retreatability of Root Canals Obturated with Two Different Techniques

by Tareq Azeez Hamzah and Maha Mosaid Yahya Albazzaz

Eng. Proc. 2023, 59(1), 246; https://doi.org/10.3390/engproc2023059246 - 6 May 2024

Viewed by 2057

Abstract

The objective of this in vitro study was to compare the efficacy of three different rotary systems, namely D-RaCe, R-Endo, and Edgefile XR, in the removal of root canal obturation materials during non-surgical retreatment procedures. Lower first premolars with straight oval canals were [...] Read more.

The objective of this in vitro study was to compare the efficacy of three different rotary systems, namely D-RaCe, R-Endo, and Edgefile XR, in the removal of root canal obturation materials during non-surgical retreatment procedures. Lower first premolars with straight oval canals were utilized, and microcomputed tomography (micro-CT) was employed as an evaluation method. The study also aimed to investigate the influence of two different initial obturation methods, the single cone and the continuous wave compaction techniques, on the amount of residual material after retreatment. The findings revealed that none of the retreatment systems could completely eliminate the obturation material, corroborating existing studies. However, Edgefile XR outperformed the other systems in terms of reduced residual material. The continuous wave compaction method for initial obturation resulted in fewer remnants compared to the single cone technique. This contradicts prior research suggesting that the two methods offer comparable sealing abilities. The study underscores the advantages of using micro-CT for evaluation, as it provides a more accurate three-dimensional assessment of the residual materials in the canals. Despite its limitations, such as the focus on straight canals and the in vitro setting, the study provides crucial insights for clinicians. It suggests that the choice of rotary system and initial obturation method can significantly impact the success of root canal retreatment procedures. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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5 pages, 242 KB

Open AccessProceeding Paper

Assessment of ²²²Rn Activity in Bottled Water from Baghdad and Its Radiological Impact

by Muhannad Kh. Mohammed, Rahim Jaafar Aziz, Nabeel H. Ameen, Huda N. Karkosh and Mohammed Sh. Naji

Eng. Proc. 2023, 59(1), 247; https://doi.org/10.3390/engproc2023059247 - 6 May 2024

Viewed by 1347

Abstract

This study investigates the radiological impact of ²²²Rn activity concentrations in bottled drinking water sourced from local markets in Baghdad, Iraq. Utilizing the solid-state nuclear track detector (SSNTD) technique with CR-39 detectors, ²²²Rn activity concentrations were measured in 25 bottled water [...] Read more.

This study investigates the radiological impact of ²²²Rn activity concentrations in bottled drinking water sourced from local markets in Baghdad, Iraq. Utilizing the solid-state nuclear track detector (SSNTD) technique with CR-39 detectors, ²²²Rn activity concentrations were measured in 25 bottled water samples. Concentrations ranged from 1.5 to 11.12 Bq/L, with an average value of 4.58 Bq/L. To assess the potential health risks, the annual effective dose (AED) due to ²²²Rn ingestion was calculated. The potential radiation doses ranged from

3.21 \times 10^{- 6}

Sv/y for infants to

1.17 \times 10^{- 5}

Sv/y for adults. These values are significantly lower than the established dose limit of

0.1 \times 10^{- 3}

Sv/y, thereby indicating a negligible radiological risk to consumers. The study also explored the correlation between total dissolved solids (TDS) and ²²²Rn concentrations, finding a direct relationship between higher TDS values and elevated ²²²Rn levels. The findings of this research contribute to the understanding of natural radionuclide levels in drinking water and their implications for public health. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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2 pages, 146 KB

Open AccessRetraction

RETRACTED: Sarsia et al. Unveiling the Neural Mirage in the Pursuit of Transcendent Intelligence. Eng. Proc. 2023, 59, 102

by Pankaj Sarsia, Satyam Mishra, Aradhya Joshi, Amit Agrawal and Shristi Mishra

Eng. Proc. 2023, 59(1), 248; https://doi.org/10.3390/engproc2023059248 - 27 Oct 2025

Viewed by 343

Abstract

The journal retracts the article “Unveiling the Neural Mirage in the Pursuit of Transcendent Intelligence” [...] Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

10 pages, 2920 KB

Open AccessProceeding Paper

Prediction of Machining Characteristics and Machining Performance for Grade 2 Titanium Material in a Wire Electric Discharge Machine Using Group Method of Data Handling and Artificial Neural Network

by Sudhir Jain Prathik, Athimoolam Sundaramahalingam, Maddur Eswara Nithyashree, Addamani Rudreshi and Gonchikar Ugrasen

Eng. Proc. 2023, 59(1), 9085; https://doi.org/10.3390/engproc2023059085 - 19 Dec 2023

Cited by 2 | Viewed by 1304

Abstract

The present research focuses on the machining of grade 2 titanium material using the Wire Electric Discharge Machining (WEDM) process by means of L₁₆ Orthogonal Array (OA). This study investigates numerous process parameters, including pulse on time, current, pulse off time, voltage, [...] Read more.

The present research focuses on the machining of grade 2 titanium material using the Wire Electric Discharge Machining (WEDM) process by means of L₁₆ Orthogonal Array (OA). This study investigates numerous process parameters, including pulse on time, current, pulse off time, voltage, bed speed and flush rate. The voltage and flush rate were kept constant throughout the experiment, while the other four parameters were varied for the machining process. In this study, a 0.18 mm molybdenum wire was utilized as the electrode material. Initially, this research aimed to optimize the process parameters to discern their impact on machining characteristics (Surface Roughness and Electrode Wear) as well as on machining performance (Acoustic Emission Signals). Subsequently, simpler functional relationship plots were generated between these parameters to recognize the potential information about the machining characteristics and machining performance. The straightforward approach lacks the capability to furnish information regarding the condition of the material (Surface Roughness), the tool (Electrode Wear) and the signals (Acoustic Emission). Hence, to estimate the experimental values the numerical tools viz., Group Method of Data Handling (GMDH) and Artificial Neural Network (ANN) were used. Upon comparing the predictive performance of ANN and GMDH, it became evident that the ANN’s predictions using 70% of the data for training displayed a higher correlation with the experimental values compared to the GMDH. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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9 pages, 1690 KB

Open AccessProceeding Paper

A Novel Hyper-Spectral Model to Optimize the Prediction Rate for Heart Disease in Modern Healthcare Networks

by K. Abinaya, Damodharan Palaniappan and M. Vedaraj

Eng. Proc. 2023, 59(1), 59081; https://doi.org/10.3390/engproc2023059081 - 19 Dec 2023

Cited by 3 | Viewed by 1417

Abstract

Coronary heart disease is one of the most extreme and leading causes of death globally. Heart disease’s correct and timely prognosis is crucial to prevent further morbidity and mortality. In recent years, the appearance of present-day healthcare has led to analyzing and improving [...] Read more.

Coronary heart disease is one of the most extreme and leading causes of death globally. Heart disease’s correct and timely prognosis is crucial to prevent further morbidity and mortality. In recent years, the appearance of present-day healthcare has led to analyzing and improving the latest diagnostic models for heart ailments. Hyper-spectral imaging methods are rising as appropriate and reliable techniques for heart ailment prediction. This paper presents an optimized hyper-spectral model for coronary heart disorder prediction, which uses both depth and spatial features. The proposed version extracts depth and spatial features to recognize heart ailments in clinical scans. The depth function extraction layer is designed to phase the scans and become aware of suspicious areas with anomalies. The spatial feature extraction layer is designed to obtain the features in those areas for similar spatial analysis. The extracted functions are then used to train a primarily CNN-based version on the type of coronary heart disorder. The effects of the proposed version were tested and compared with other existing methods and determined to be correct. The proposed model is robust and presents a greater accuracy and better overall performance for heart disorder analysis. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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7 pages, 986 KB

Open AccessProceeding Paper

Removal of Brilliant-Green Dye Using Carbon-Loaded Zinc Oxide Nanoparticles: A Comparative Isotherm Study

by Ayad F. Alkaim, Ahmed B. Mahdi, Usama S. Altimari, Shadha Al Qaysi, Montather F. Ramadan and Aseel M. Aljeboree

Eng. Proc. 2023, 59(1), 59152; https://doi.org/10.3390/engproc2023059152 - 10 Jan 2024

Cited by 3 | Viewed by 1612

Abstract

Adsorption is a phase transfer process extensively utilized for removing substances from fluid phases (either gases or liquids) to the solid phase, also known as the adsorbent particle. This natural method is observable in various environmental compartments. In water or effluent treatments, a [...] Read more.

Adsorption is a phase transfer process extensively utilized for removing substances from fluid phases (either gases or liquids) to the solid phase, also known as the adsorbent particle. This natural method is observable in various environmental compartments. In water or effluent treatments, a solid interacts with a pollutant, such as a dye. The pollutant is termed the adsorbate, and the solid is the adsorbent. This technique has been proven efficient in removing a broad range of contaminants. This study investigates the use of the adsorption technique to eliminate brilliant-green dye from aqueous solutions, employing different adsorbent materials like AC, CNT, ZnO, and ZnO/AC prepared through the hydrothermal method. The compositions of these composites were elucidated using analytical techniques such as FTIR, EDX, and FE-SEM. The study also compares the efficiency of different carbon sources in removing brilliant-green dye, namely, activated carbon (AC), carbon nanotubes (CNTs), zinc oxide (ZnO), and AC/ZnO nanocomposites as adsorbents. The removal efficiency (E%) for BG dye followed the order: CNT > ZnO/AC > AC > ZnO. Additionally, a comparison was made between sonication and a shaker water bath for different carbon sources in removing brilliant-green dye. The shaker water bath demonstrated an efficiency range of 90.122% to 42.812%, while sonication showed 90.011% to 32.012%. The adsorption data aligned with the Freundlich isotherm model. Full article

(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)

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