Information

13 pages, 3483 KiB

Open AccessArticle

Classification of English Words into Grammatical Notations Using Deep Learning Technique

by Muhammad Imran, Sajjad Hussain Qureshi, Abrar Hussain Qureshi and Norah Almusharraf

Information 2024, 15(12), 801; https://doi.org/10.3390/info15120801 - 11 Dec 2024

The impact of artificial intelligence (AI) on English language learning has become the center of attention in the past few decades. This study, with its potential to transform English language instruction and offer various instructional approaches, provides valuable insights and knowledge. To fully [...] Read more.

The impact of artificial intelligence (AI) on English language learning has become the center of attention in the past few decades. This study, with its potential to transform English language instruction and offer various instructional approaches, provides valuable insights and knowledge. To fully grasp the potential advantages of AI, more research is needed to improve, validate, and test AI algorithms and architectures. Grammatical notations provide a word’s information to the readers. If a word’s images are properly extracted and categorized using a CNN, it can help non-native English speakers improve their learning habits. The classification of parts of speech into different grammatical notations is the major problem that non-native English learners face. This situation stresses the need to develop a computer-based system using a machine learning algorithm to classify words into proper grammatical notations. A convolutional neural network (CNN) was applied to classify English words into nine classes: noun, pronoun, adjective, determiner, verb, adverb, preposition, conjunction, and interjection. A simulation of the selected model was performed in MATLAB. The model achieved an overall accuracy of 97.22%. The CNN showed 100% accuracy for pronouns, determiners, verbs, adverbs, and prepositions; 95% for nouns, adjectives, and conjunctions; and 90% for interjections. The significant results (p < 0.0001) of the chi-square test supported the use of the CNN by non-native English learners. The proposed approach is an important source of word classification for non-native English learners by putting the word image into the model. This not only helps beginners in English learning but also helps in setting standards for evaluating documents. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)

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18 pages, 3254 KiB

Open AccessArticle

Design and Implementation of an Immersive Web-Based Digital Twin Steam Turbine System for Industrial Training

by Zhe Li, Hui Xiao, Bo Wang, Xuzhu Dong, Lianteng Shen, Xiaomeng Di and Xiaodong Du

Information 2024, 15(12), 800; https://doi.org/10.3390/info15120800 - 11 Dec 2024

Abstract

The steam turbine and its digital electro-hydraulic (DEH) control system constitute vital elements within thermal power generation. However, the complexity of the on-site environment and the high production costs of the equipment hinder users, especially novices, from fully understanding and mastering the operation [...] Read more.

The steam turbine and its digital electro-hydraulic (DEH) control system constitute vital elements within thermal power generation. However, the complexity of the on-site environment and the high production costs of the equipment hinder users, especially novices, from fully understanding and mastering the operation mechanisms and production processes. In the realm of emerging technologies, the digital twin stands out as a powerful tool for enhancing industrial training and learning for students and operators in this field. This paper details the design and implementation of a web-based digital twin steam turbine system. Initially, a pioneering web-based digital twin architecture is proposed, featuring high-fidelity equipment modeling, web-based immersive 3D displays, algorithm design and networked implementation, and data-driven model synchronization. Subsequently, the functionalities and benefits of the digital twin system in facilitating industrial training are explained, covering aspects such as steam turbine cognitive learning, DEH system simulation learning, and condition monitoring. Finally, a case study in a real thermal power plant is presented to demonstrate the practicability and effectiveness of this web-based digital twin system. This research endeavors to contribute valuable insights and potential solutions to the growing field of web-based digital twin applications in industry. Full article

(This article belongs to the Section Information and Communications Technology)

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28 pages, 11283 KiB

Open AccessArticle

Spatial Planning Strategies for Urban Ecology and Heritage Conservation in Macau: An Investigation of Ultra-High-Density Cities

by Jitai Li, Fan Lin, Shuai Yang and Yile Chen

Information 2024, 15(12), 799; https://doi.org/10.3390/info15120799 - 11 Dec 2024

Abstract

Urban ecology and heritage-protection space are important parts of urban land and should be highly emphasized in urban planning. Improving and upgrading the level of urban ecological and heritage-conservation space will directly affect the level of spatial quality of the built environment of [...] Read more.

Urban ecology and heritage-protection space are important parts of urban land and should be highly emphasized in urban planning. Improving and upgrading the level of urban ecological and heritage-conservation space will directly affect the level of spatial quality of the built environment of the city. As a high-density city, Macau suffers from “urban diseases” such as a low per capita green space rate and poor quality of the human environment. Therefore, in order to solve this kind of urban disease, the green environment quality of Macau’s heritage space should be improved and more leisure and recreational space should be provided for urban residents. This paper examines the layout of cultural heritage open spaces in Macau and suggests strategies for planning the preservation and use of these critical resources. This study investigated the current status of open space resources in Macau’s offshore islands, cultural heritage space resources, eight Largo spaces (“Largo” is a unique term for urban public open space in Macau) in Macau’s historic city, site-based cultural heritage open spaces, and the current status of rural walking trails in the offshore islands’ ecological reserves, and used GIS technology to visualize the survey results. Last, this paper investigates the planning strategy for the sustainable use of Macau’s outlying island ecological reserves and proposes a “green network” to connect the main mountains, green areas, and coastal wetlands in Coloane with the natural space. In view of the heritage-conservation space of the Macau Peninsula, this paper proposes the planning strategy of constructing linear landscape and cultural routes and planning characteristic heritage streets and alleys. Through using cultural heritage buildings as nodes and connecting pedestrian streets and alleys, the planning mode of combining heritage attractions and streets and alleys is realized. Full article

(This article belongs to the Special Issue Techniques and Data Analysis in Cultural Heritage)

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27 pages, 547 KiB

Open AccessArticle

Cybersecurity as a Contributor Toward Resilient Internet of Things (IoT) Infrastructure and Sustainable Economic Growth

by Georgia Dede, Anastasia Maria Petsa, Stelios Kavalaris, Emmanouil Serrelis, Spyridon Evangelatos, Ioannis Oikonomidis and Thomas Kamalakis

Information 2024, 15(12), 798; https://doi.org/10.3390/info15120798 - 11 Dec 2024

Abstract

This paper investigates the contribution of the various cybersecurity domains to the United Nations’ Sustainable Development Goals (SDGs), emphasizing the critical role of cybersecurity in advancing sustainable economic growth and resilient IoT infrastructure. The paper also examines specific use cases on how cybersecurity [...] Read more.

This paper investigates the contribution of the various cybersecurity domains to the United Nations’ Sustainable Development Goals (SDGs), emphasizing the critical role of cybersecurity in advancing sustainable economic growth and resilient IoT infrastructure. The paper also examines specific use cases on how cybersecurity measures and practices can contribute to achieving SDG 8 and SDG 9 focused on decent work and economic growth and industry, innovations, and infrastructure. In the context of SDG 8 the use case of a smart agriculture network was examined, whereas for SDG 9, the use case focuses on a smart factory processing raw materials. An analysis of the prioritization of the several cybersecurity domains following the MoSCoW method is also presented. This paper offers valuable insights and guidance for enhancing corporate resilience and economic benefits in the Internet of Things (IoT) aligning with the SDGs and contributing to a more sustainable and resilient future for the IoT. Full article

(This article belongs to the Special Issue Technoeconomics of the Internet of Things)

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20 pages, 5729 KiB

Open AccessArticle

Context-Aware QoE for Mobility-Driven Applications Through Dynamic Surveys

by Omer Nawaz, Yuan Zhou, Siamak Khatibi and Markus Fiedler

Information 2024, 15(12), 797; https://doi.org/10.3390/info15120797 - 11 Dec 2024

Abstract

The integration of outdoor smartphone applications with fitness trackers has introduced new opportunities and challenges for user interaction, particularly in mobility-driven activities. While these innovations offer significant benefits, they also pose challenges due to the many factors influencing the user’s quality of experience. [...] Read more.

The integration of outdoor smartphone applications with fitness trackers has introduced new opportunities and challenges for user interaction, particularly in mobility-driven activities. While these innovations offer significant benefits, they also pose challenges due to the many factors influencing the user’s quality of experience. Traditional methods of assessing user experiences, such as offline surveys and static questionnaires, often fail to capture the dynamic nature of outdoor activities. This research proposes a novel Quality of Experience (QoE) methodology for mobile applications to enhance the assessment of user experiences in cycling. Focusing on a use case in Blekinge, Sweden, where residents were encouraged to adopt cycling for daily transportation, we extracted land cover data and developed a server-side workflow for bicycle path segmentation. By incorporating dynamic surveys that adapt to users’ real-time experiences, we aim to generate a more accurate and context-aware dataset. This study makes several key contributions: First, it presents a scalable method for bicycle path segmentation; second, it demonstrates the utility and benefits of land cover data extraction; and finally, it evaluates the effectiveness of QoE influence factors through user surveys based on real-world cyclist feedback. This approach is expected to enhance the planning and development of cycling infrastructure by providing urban planners and stakeholders with valuable user insights using adaptable surveys based on route segmentation. Full article

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18 pages, 2010 KiB

Open AccessArticle

Research on Evolutionary Game of Adopting Blockchain-Based Automotive Traceability

by Xiaoyu Tang, Shouwei Li, Jingwei Li and Yonghong Zhang

Information 2024, 15(12), 796; https://doi.org/10.3390/info15120796 - 11 Dec 2024

Abstract

To adopt the blockchain-based automobile traceability system (BCATS) and increase the transparency of the Chinese auto market, this study constructs a tripartite evolutionary game model of manufacturers, regulators, and consumers, discusses the evolutionary stabilization strategy (ESS) under different cases, and analyzes the influencing [...] Read more.

To adopt the blockchain-based automobile traceability system (BCATS) and increase the transparency of the Chinese auto market, this study constructs a tripartite evolutionary game model of manufacturers, regulators, and consumers, discusses the evolutionary stabilization strategy (ESS) under different cases, and analyzes the influencing factors on the tripartite ESS through numerical simulation. The study finds that there exists an ESS of blockchain adoption and tripartite cooperation, and it is influenced by different factors including blockchain construction cost, traceability service price, and government subsidy. Lowering the blockchain construction cost, and increasing the traceability service price and government subsidy can all have a positive impact on accomplishing the ideal ESS, but the latter two can also have a negative impact when they are beyond the scope of effectiveness. The study results provide practical recommendations for adopting blockchain in the auto traceability, which can help to promote blockchain in the Chinese auto market. Full article

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20 pages, 19406 KiB

Open AccessArticle

Research on the Application of Topic Models Based on Geological Disaster Information Mining

by Gang Cheng, Qinliang You, Gangqiang Li, Youcai Li, Daisong Yang, Jinghong Wu and Yaxi Wu

Information 2024, 15(12), 795; https://doi.org/10.3390/info15120795 - 10 Dec 2024

Abstract

Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to [...] Read more.

Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of “Dongting Lake breach” in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining. Full article

(This article belongs to the Section Information Processes)

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30 pages, 3486 KiB

Open AccessArticle

Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms

by Jongho Seol, Jongyeop Kim and Abhilash Kancharla

Information 2024, 15(12), 794; https://doi.org/10.3390/info15120794 - 10 Dec 2024

Abstract

This computational study investigates dynamic self-healing processes in nanomaterials driven by neuronal spike activity. We developed a multiscale simulation framework that integrates neuronal dynamics, quantum mechanical effects, and material science principles. Our model incorporates a time-dependent neuron spike voltage equation coupled with a [...] Read more.

This computational study investigates dynamic self-healing processes in nanomaterials driven by neuronal spike activity. We developed a multiscale simulation framework that integrates neuronal dynamics, quantum mechanical effects, and material science principles. Our model incorporates a time-dependent neuron spike voltage equation coupled with a nanomaterial health update function, including quantum probability terms, to capture nanoscale effects. We employ reliability engineering concepts to assess system performance. Simulations reveal that neuronal spike patterns significantly influence self-healing dynamics, exhibiting non-linear behavior with quantum effects crucial to healing efficiency. Statistical analysis demonstrates a strong correlation between spike frequency and healing rate, identifying an optimal range for maximum recovery. Integrating quantum probabilities yields more accurate nanoscale behavior predictions than classical approaches alone. This study provides a foundation for understanding and optimizing neuronal spike-induced recovery in nanomaterials with potential applications in neural interfaces, intelligent materials, and biomedical devices. Full article

(This article belongs to the Section Information Theory and Methodology)

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15 pages, 428 KiB

Open AccessArticle

Recognition of Industrial Spare Parts Using an Optimized Convolutional Neural Network Model

by Chandralekha Mohan, Takfarinas Saber and Priyadharshini Jayadurga Nallathambi

Information 2024, 15(12), 793; https://doi.org/10.3390/info15120793 - 10 Dec 2024

Abstract

Spare parts search and retrieval processes are of paramount importance in manufacturing and supply chains. Image recognition using 2D and 3D image properties plays an important part in the success of such processes, as it facilitates the identification of the types and components [...] Read more.

Spare parts search and retrieval processes are of paramount importance in manufacturing and supply chains. Image recognition using 2D and 3D image properties plays an important part in the success of such processes, as it facilitates the identification of the types and components associated with spare parts, a step that is crucial for their success. In this article, a novel Deep Learning-based object recognition model based on a convolutional neural network architecture is proposed and constructed using stacked convolutional layers to extract and learn features of the spare parts efficiently with the goal of improving the effectiveness of the spare part image recognition process. The proposed model is assessed using industrial spare parts datasets, and its performance is compared against different transfer learning models using precision, accuracy, recall, and F1 score. The proposed model demonstrated efficiency in spare parts recognition and achieved the highest accuracy compared to state-of-the-art image recognition models. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)

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23 pages, 1262 KiB

Open AccessArticle

Leveraging Large Language Models in Tourism: A Comparative Study of the Latest GPT Omni Models and BERT NLP for Customer Review Classification and Sentiment Analysis

by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas and Dimitrios K. Nasiopoulos

Information 2024, 15(12), 792; https://doi.org/10.3390/info15120792 - 10 Dec 2024

Abstract

In today’s rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer satisfaction, enabling businesses to gain a competitive edge. This [...] Read more.

In today’s rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer satisfaction, enabling businesses to gain a competitive edge. This study undertakes a comparative analysis of traditional natural language processing (NLP) models, such as BERT and advanced large language models (LLMs), specifically GPT-4 omni and GPT-4o mini, both pre- and post-fine-tuning with few-shot learning. By leveraging an extensive dataset of hotel reviews, we evaluate the effectiveness of these models in predicting star ratings based on review content. The findings demonstrate that the GPT-4 omni family significantly outperforms the BERT model, achieving an accuracy of 67%, compared to BERT’s 60.6%. GPT-4o, in particular, excelled in accuracy and contextual understanding, showcasing the superiority of advanced LLMs over traditional NLP methods. This research underscores the potential of using sophisticated review evaluation systems in the hospitality industry and positions GPT-4o as a transformative tool for sentiment analysis. It marks a new era in automating and interpreting customer feedback with unprecedented precision. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU))

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21 pages, 4145 KiB

Open AccessArticle

UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection

by Jianmei Zhong and Yanzhi Song

Information 2024, 15(12), 791; https://doi.org/10.3390/info15120791 - 10 Dec 2024

Abstract

Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which typically suffer significant performance drops in [...] Read more.

Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which typically suffer significant performance drops in multi-class scenarios. Research specifically targeting multi-class anomaly detection remains relatively limited. In this work, we propose a powerful unified normalizing flow for multi-class anomaly detection, which we call UniFlow. A multi-cognitive visual adapter (Mona) is employed in our method as the feature adaptation layer to adapt image features for both the multi-class anomaly detection task and the normalizing flow model, facilitating the learning of general knowledge of normal images across multiple categories. We adopt multi-cognitive convolutional networks with high capacity to construct the coupling layers within the normalizing flow model for more effective multi-class distribution modeling. In addition, we employ a multi-scale feature fusion module to aggregate features from various levels, thereby obtaining fused features with enhanced expressive capabilities. UniFlow achieves a class-average image-level AUROC of 99.1% and a class-average pixel-level AUROC of 98.0% on MVTec AD, outperforming the SOTA multi-class anomaly detection methods. Extensive experiments on three benchmark datasets, MVTec AD, VisA, and BTAD, demonstrate the efficacy and superiority of our unified normalizing flow in multi-class anomaly detection. Full article

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22 pages, 3412 KiB

Open AccessArticle

Educational Cyber–Physical Systems (ECPSs) for University 4.0

by Laurent Gallon, Khouloud Salameh, Richard Chbeir, Samia Bachir and Philippe Aniorté

Information 2024, 15(12), 790; https://doi.org/10.3390/info15120790 - 9 Dec 2024

Abstract

University 4.0 represents the adaptation of the Education 4.0 paradigm to the university context. The core principle is the automated supervision of the entire student learning process by an AI-driven computer assistant, allowing for timely adjustments based on the student’s progression. Critical to [...] Read more.

University 4.0 represents the adaptation of the Education 4.0 paradigm to the university context. The core principle is the automated supervision of the entire student learning process by an AI-driven computer assistant, allowing for timely adjustments based on the student’s progression. Critical to this process is the assistant’s ability to collect comprehensive information on all student activities within the curriculum, particularly overseeing pedagogical activities in real time to make necessary adaptations. This utilizes Educational Cyber–Physical Systems (ECPSs) to gather all relevant data and extract appropriate information effectively. This article examines a specific case of practical work involving students at two distinct geographical locations collaborating in a blended learning environment. A specialized ECPS is deployed to collect data from equipment at both sites, enabling the modeling of the pedagogical sequence, the cyber–physical system, and the data necessary for monitoring student progress in real time. Full article

(This article belongs to the Special Issue Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence)

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24 pages, 38325 KiB

Open AccessArticle

A Yoga Pose Difficulty Level Estimation Method Using OpenPose for Self-Practice System to Yoga Beginners

by Cheng-Liang Shih, Jun-You Liu, Irin Tri Anggraini, Yanqi Xiao, Nobuo Funabiki and Chih-Peng Fan

Information 2024, 15(12), 789; https://doi.org/10.3390/info15120789 - 9 Dec 2024

Abstract

Yoga is an exercise preferable for various users at different ages to enhance physical and mental health. To help beginner yoga self-practitioners avoid getting injured by selecting difficult yoga poses, the information of the difficulty level of yoga poses is very important to [...] Read more.

Yoga is an exercise preferable for various users at different ages to enhance physical and mental health. To help beginner yoga self-practitioners avoid getting injured by selecting difficult yoga poses, the information of the difficulty level of yoga poses is very important to provide an objective metric to assist yoga self-practitioners in selecting appropriate exercises on the basis of their skill level by using the yoga self-practice system. To enhance the developed yoga self-practice system, the yoga difficulty level estimation function will enable users to clearly understand whether the selected yoga poses are suitable for them. In this paper, the newest difficulty level estimation method of yoga poses is proposed by using and analyzing OpenPose two-dimensional (2D) human body keypoints. The proposed method effectively uses the selected six keypoints areas of the upper and lower body, body support types, center of gravity calculations, and body tilt angles and slopes to produce estimations. Firstly, the method calculates the weighted centers of the upper and lower human body for each pose by using keypoints. Secondly, it refers the slope of the centroid line between the two centers and infers the body’s balance state. Lastly, the system estimates the difficulty level by additionally considering the keypoints of the body to contact the ground. For evaluations of the proposal, more than one hundred yoga poses are collected from the Internet and applied to classify them into five difficulty levels. Through comparisons with subjective levels from one instructor and 10 users, the validity of the estimation results is confirmed, a comparison is performed with existing designs, and it is implemented in embedded systems. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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21 pages, 7942 KiB

Open AccessArticle

Application of ResUNet-CBAM in Thin-Section Image Segmentation of Rocks

by Ling Zhao, Huili Zhang, Xianda Sun, Zhaozhuo Ouyang, Chengwu Xu and Xudong Qin

Information 2024, 15(12), 788; https://doi.org/10.3390/info15120788 - 9 Dec 2024

Abstract

The convolutional neural network (CNN) algorithm in deep learning has been widely applied in petroleum geology research both domestically and internationally. Automated and accurate segmentation of thin-section images of rocks is foundational for in-depth analysis. However, traditional segmentation methods for reservoir rock thin [...] Read more.

The convolutional neural network (CNN) algorithm in deep learning has been widely applied in petroleum geology research both domestically and internationally. Automated and accurate segmentation of thin-section images of rocks is foundational for in-depth analysis. However, traditional segmentation methods for reservoir rock thin sections often suffer from low accuracy and high cost. To address these issues, this paper proposes a novel segmentation algorithm based on an improved UNet network, integrating residual networks and the CBAM attention mechanism. By incorporating residual modules, the network depth is expanded, and the CBAM attention mechanism enhances the feature weighting capability during learning. Experimental results demonstrate that this method outperforms traditional approaches in both segmentation accuracy and efficiency, representing significant advancements in reservoir rock thin-section image segmentation. Full article

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17 pages, 8899 KiB

Open AccessArticle

Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia

by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan and Anam Naz

Information 2024, 15(12), 787; https://doi.org/10.3390/info15120787 - 8 Dec 2024

Abstract

Medical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strategies, especially [...] Read more.

Medical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strategies, especially in life-threatening diseases such as leukemia. Leukemia, a blood malignancy, is one of the most prevalent cancer types affecting both adults and children. It is caused by the rapid and uncontrolled growth of abnormal white blood cells in the bone marrow. This accumulation interferes with the production of normal blood cells, leading to a weakened immune deficiency, anemia, and bleeding disorders. Conventional leukemia diagnostic methods are time-consuming, manually intensive, and inefficient. This research study proposes an automatic diagnostics prediction of leukemia by analyzing blood images according to the shape of the blast cells using digital image processing and machine learning. The purpose of blood cell detection is to precisely identify and classify diverse blood cells, detecting anomalies associated with blood cancers like leukemia. This supports early diagnosis and monitoring, which leads to more effective treatments and improved results for cancer patients. To accomplish this task, we use digital image processing techniques and then apply the convolutional neural network (CNN) deep learning algorithm to blood sample images. This research employs a multi-stage methodology, including data preparation, data preprocessing, feature extraction, and then classification. While our model is built on a typical CNN architecture, we make significant advances by using preprocessing techniques and hyperparameter tuning. We have modified its layers combination to include convolutional, pooling, and fully connected layers that are optimized for image characteristics. These layers are fine-tuned for better feature extraction and classification accuracy. This study showed that blood cell detection for diagnosing acute leukemia based on images had 99% accuracy and outperformed other advanced models, including DenseNet121, ResNet-50, Incep-tionv3, MobileNet, and EfficientNet. The comprehensive analysis of the results reveals the highest accuracy of leukemia detection as compared to existing studies in the relevant literature. Full article

(This article belongs to the Special Issue Transformative Technologies in Healthcare: Harnessing Machine Learning, Deep Learning and Large Language Models in Health Informatics)

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24 pages, 830 KiB

Open AccessSystematic Review

Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection

by Gurgen Hovakimyan and Jorge Miguel Bravo

Information 2024, 15(12), 786; https://doi.org/10.3390/info15120786 - 7 Dec 2024

Abstract

In this comprehensive literature review, we rigorously adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for our process and reporting. This review employs an innovative method integrating the advanced natural language processing model T5 (Text-to-Text Transfer Transformer) to [...] Read more.

In this comprehensive literature review, we rigorously adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for our process and reporting. This review employs an innovative method integrating the advanced natural language processing model T5 (Text-to-Text Transfer Transformer) to enhance the accuracy and efficiency of screening and data extraction processes. We assess strategies for handling the concept drift in machine learning using high-impact publications from notable databases that were made accessible via the IEEE and Science Direct APIs. The chronological analysis covering the past two decades provides a historical perspective on methodological advancements, recognizing their strengths and weaknesses through citation metrics and rankings. This review aims to trace the growth and evolution of concept drift mitigation strategies and to provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field. Key findings highlight the effectiveness of diverse methodologies such as drift detection methods, window-based methods, unsupervised statistical methods, and neural network techniques. However, challenges remain, particularly with imbalanced data, computational efficiency, and the application of concept drift detection to non-tabular data like images. This review aims to trace the growth and evolution of concept drift mitigation strategies and provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field. Full article

(This article belongs to the Topic Decision-Making and Data Mining for Sustainable Computing)

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13 pages, 2724 KiB

Open AccessArticle

Model Optimization and Dynamic Analysis of Inventory Management in Manufacturing Enterprises

by Tengfei Lei, Rita Yi Man Li and Jirawan Deeprasert

Information 2024, 15(12), 785; https://doi.org/10.3390/info15120785 - 7 Dec 2024

Abstract

This study investigates inventory management systems using a sample of listed manufacturing companies in China from 2019 to 2023. By constructing a static mathematical model, the impact of inventory management on corporate performance was empirically tested. Additionally, based on a classical inventory management [...] Read more.

This study investigates inventory management systems using a sample of listed manufacturing companies in China from 2019 to 2023. By constructing a static mathematical model, the impact of inventory management on corporate performance was empirically tested. Additionally, based on a classical inventory management dynamical model and considering inventory delay characteristics, a new class of two-dimensional inventory management systems was reconstructed. The system’s periodic and chaotic nonlinear characteristics were verified using 0-1 tests, bifurcation diagrams, Lyapunov exponents, and system eigenvalue plots. Furthermore, MATLAB simulations were employed to examine the effect of resource transfer rates on the nonlinear dynamic behavior of the inventory management system. The results from both static mathematical models and dynamical models provide a theoretical basis for inventory management and safety stock level predictions in the manufacturing industry. Full article

(This article belongs to the Section Information Applications)

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33 pages, 982 KiB

Open AccessArticle

Implementation of the Asset Management, Operational Reliability and Maintenance Survey in Recycled Beverage Container Manufacturing Lines

by Carlos Parra, Carlos Morán, Félix Pizarro, Pablo Duque, Andrés Aránguiz, Vicente González-Prida and Jorge Parra

Information 2024, 15(12), 784; https://doi.org/10.3390/info15120784 - 6 Dec 2024

Abstract

The effectiveness of a comprehensive maintenance and reliability management process can be assessed through an in-depth analysis of various factors that collectively represent the maintenance contribution to the operational and production processes of an industrial asset. There are no simple formulas for designing [...] Read more.

The effectiveness of a comprehensive maintenance and reliability management process can be assessed through an in-depth analysis of various factors that collectively represent the maintenance contribution to the operational and production processes of an industrial asset. There are no simple formulas for designing an integrated maintenance and reliability model within an asset management framework (in accordance with the ISO 55001 standard), nor are there fixed or universal rules that apply equally to all production assets over time. In light of this, the primary goal of this article is to provide an overview of the implementation project of the AMORMS (Asset Management, Operational Reliability and Maintenance Survey), based on the maintenance management model (MMM) developed by INGEMAN, at the SINEA PERU plant, a leading company in Latin America specializing in the industrial production of recycled plastic containers for commercial beverages (PET preforms—Polyethylene Terephthalate). Lastly, the article outlines the recommendations and high-impact action plans that will support SINEA PERU in strengthening the efficiency of its maintenance and reliability management processes while effectively optimizing the value of its industrial assets throughout its life cycle. Full article

(This article belongs to the Special Issue New Applications in Multiple Criteria Decision Analysis II)

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19 pages, 12083 KiB

Open AccessArticle

An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection

by Flavia Grignaffini, Enrico De Santis, Fabrizio Frezza and Antonello Rizzi

Information 2024, 15(12), 783; https://doi.org/10.3390/info15120783 - 5 Dec 2024

Abstract

Computer-aided diagnosis (CAD) systems, which combine medical image processing with artificial intelligence (AI) to support experts in diagnosing various diseases, emerged from the need to solve some of the problems associated with medical diagnosis, such as long timelines and operator-related variability. The most [...] Read more.

Computer-aided diagnosis (CAD) systems, which combine medical image processing with artificial intelligence (AI) to support experts in diagnosing various diseases, emerged from the need to solve some of the problems associated with medical diagnosis, such as long timelines and operator-related variability. The most explored medical application is cancer detection, for which several CAD systems have been proposed. Among them, deep neural network (DNN)-based systems for skin cancer diagnosis have demonstrated comparable or superior performance to that of experienced dermatologists. However, the lack of transparency in the decision-making process of such approaches makes them “black boxes” and, therefore, not directly incorporable into clinical practice. Trying to explain and interpret the reasons for DNNs’ decisions can be performed by the emerging explainable AI (XAI) techniques. XAI has been successfully applied to DNNs for skin lesion image classification but never when additional information is incorporated during network training. This field is still unexplored; thus, in this paper, we aim to provide a method to explain, qualitatively and quantitatively, a convolutional neural network model with feature injection for melanoma diagnosis. The gradient-weighted class activation mapping and layer-wise relevance propagation methods were used to generate heat maps, highlighting the image regions and pixels that contributed most to the final prediction. In contrast, the Shapley additive explanations method was used to perform a feature importance analysis on the additional handcrafted information. To successfully integrate DNNs into the clinical and diagnostic workflow, ensuring their maximum reliability and transparency in whatever variant they are used is necessary. Full article

(This article belongs to the Special Issue Transformative Technologies in Healthcare: Harnessing Machine Learning, Deep Learning and Large Language Models in Health Informatics)

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