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2 pages, 526 KiB

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 KiB

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 KiB

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

Viewed by 2054

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 KiB

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

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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 KiB

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 2 | Viewed by 2878

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 KiB

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 KiB

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 2 | Viewed by 900

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 KiB

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 2 | Viewed by 1274

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 KiB

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

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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 KiB

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 1 | Viewed by 1146

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 KiB

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

Viewed by 4842

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 KiB

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 3 | Viewed by 1762

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 KiB

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

Viewed by 902

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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1309 KiB

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 1 | Viewed by 771

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 KiB

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 965

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 KiB

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 606

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 KiB

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

Viewed by 884

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 KiB

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 1 | Viewed by 1605

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 KiB

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

Viewed by 1224

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 KiB

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

Viewed by 776

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 KiB

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 3 | Viewed by 2924

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 KiB

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 2 | Viewed by 2777

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 KiB

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 2 | Viewed by 1464

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 KiB

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

Viewed by 708

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 KiB

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 792

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 KiB

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 8 | Viewed by 2420

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 KiB

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 1 | Viewed by 1120

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 KiB

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 1 | Viewed by 1269

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 KiB

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 1 | Viewed by 1455

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 KiB

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 2 | Viewed by 6242

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 KiB

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 792

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 KiB

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 2 | Viewed by 1037

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 KiB

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 1 | Viewed by 944

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 KiB

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 1 | Viewed by 919

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 KiB

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 741

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 KiB

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 2 | Viewed by 1006

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 KiB

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

Viewed by 640

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 KiB

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

Viewed by 1277

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 KiB

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 8 | Viewed by 6491

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 KiB

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

Viewed by 1416

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 KiB

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 3 | Viewed by 3160

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 KiB

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 1195

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 KiB

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 666

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 KiB

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 754

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 KiB

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 10 | Viewed by 1024

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 KiB

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 1 | Viewed by 1113

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 KiB

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

Viewed by 847

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 KiB

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 3 | Viewed by 3266

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 KiB

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 1 | Viewed by 1098

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 KiB

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

Viewed by 1582

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 KiB

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 1 | Viewed by 986

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 KiB

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

Viewed by 796

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 KiB

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 1229

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 KiB

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 742

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 KiB

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 2 | Viewed by 770

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 KiB

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 1516

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 KiB

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 1240

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 KiB

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 1 | Viewed by 1145

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 KiB

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 2 | Viewed by 2137

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 KiB

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 1 | Viewed by 1304

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 KiB

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 3 | Viewed by 2592

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 KiB

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 1059

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 KiB

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 1 | Viewed by 1079

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 KiB

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

Viewed by 813

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 KiB

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 26 | Viewed by 2129

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 KiB

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 1 | Viewed by 1822

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 KiB

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 1 | Viewed by 1672

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 KiB

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 1103

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 KiB

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

Viewed by 802

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 KiB

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 3 | Viewed by 9947

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 KiB

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 25 | Viewed by 1743

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 KiB

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 1 | Viewed by 1026

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 KiB

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 3 | Viewed by 1259

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 KiB

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 1 | Viewed by 1359

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 KiB

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 1 | Viewed by 1341

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 KiB

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 1 | Viewed by 701

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 KiB

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 829

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 KiB

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

Viewed by 1097

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 KiB

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

Viewed by 629

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 KiB

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 1 | Viewed by 1232

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 KiB

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

Viewed by 863

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 KiB

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 681

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 KiB

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 3 | Viewed by 3325

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 KiB

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

Viewed by 1238

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 KiB

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 7 | Viewed by 2175

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 KiB

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 3 | Viewed by 1291

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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