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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Garg, Kritika Sarker, Rahul Ahmad, Aaqib Kaur, Mehakpreet Teja, A.V. Ravi Dept. of Electrical Engineering IIT Ropar Rupnagar India Dept. of Computer Science Engineering PU Patiala India

ISBN: (纸本)9781665464543

This document is a model with an accurate estimation of the battery’s State Of Charge (SOC), which is pivotal for prime performance and optimal lifespan of the rechargeable batteries. Though traditional methods, like Open Circuit Voltage (OCV) and Coulomb Counting (CC), are advantageous, they suffer from several limitations, such as sensitivity to temperature changes, frequent calibration, ageing effects, and measurement inaccuracies. This paper proposes 1-D Kalman Filter (1-D HKF) with an approach that incorporates the OCV and CC results into the state space model for better and more accurate SOC estimation. So, the model is called 1-D HKF or Hybrid Kalman filter that, influenced the long-term stability and estimation accuracy of CC and the accuracy of OCV at specific points to get over the individual limitations. The model trained by 1-D HKF adapts itself and adjusts the gain based on the uncertainties of each measured value to ensure optimal value information. So, We evaluate the performance of the 1-D HKF versus the traditional methods Kalman Filter using examples and exploratory data. The proposed approach illustrates significantly improved accuracy and robustness across various operating conditions, including varying temperatures and discharge rates. © 2024 IEEE.

关键词： State of charge

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SmartCitrus: An Efficient Deep Learning Approach for Real-Time Detection and Classification of Citrus Leaf Diseases

SmartCitrus: An Efficient Deep Learning Approach for Real-Ti...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin Orchi, Nabiha Tasnim Alam, Md Ashraful Dipto, Shakib Mahmud Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dept. of Computer Science and Engineering Dhaka Bangladesh University of Liberal Arts Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dhaka Bangladesh

ISBN: (纸本)9798350350289

Bangladesh is a prominent citrus exporter. Annually, the country has been exporting citrus fruits to over 60 countries. Distinguishing various diseases affecting citrus leaves requires a significant investment of time, effort, and specialized knowledge. Consequently, it is essential to create an innovative method for detecting citrus diseases. In this study, we have devised a valuable methodology by employing CNN models to identify diseases in citrus leaves. By employing a distinctive ensemble strategy, we successfully trained the model using varying numbers of classes in each stage. In reality, it allowed us to utilize suitable varieties of leaves for various ailments. Furthermore, it has enhanced the rate at which models learn during the later stages. In addition, it has reduced the level of model intricacy in comparison to frequently employed ensemble models. The identification of plant diseases in the present study involved the utilization of leaf photographs and algorithms for segmentation and feature extraction. Ultimately, we have successfully attained a 96 % accuracy rate for each class, signifying a substantial potential for mitigating production losses. © 2024 IEEE.

关键词： Citrus fruits

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Cluster Aware Personalized News Recommendation System Using a Global News Graph 29

Cluster Aware Personalized News Recommendation System Using ...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Araghi, Melika Sadat Shakibian, Hadi Dept. of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (纸本)9798331523114

With the increasing use of online news services and the production of a large amount of news daily, finding news content that matches user interests has become an important challenge. Personalized news recommendation systems, by modeling users’ behavior and interests, can help us to respond to this challenge. In this study, a new method for personalized news recommendations based on clustering and graph enhancement has been introduced, consisting of two primary phases. The first phase involves dividing the news graph into multiple clusters. In the second phase, recommendations are generated within each cluster. The findings indicate that this proposed approach yields more precise recommendations while also reducing computational demands. © 2025 IEEE.

关键词： Graphic methods

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Academic Use of ChatGPT: Examining the Trends According to Disciplines 8

Academic Use of ChatGPT: Examining the Trends According to D...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Bircan, Abdullah Mandal, Dilek Kosesoy, Irfan Kocaeli University Dept. of Computer Engineering Kocaeli Turkey Kocaeli University Dept. of Software Engineering Kocaeli Turkey

ISBN: (纸本)9798331531492

With the rapid advancement of large language models, particularly ChatGPT, have had a profound impact both in the academic world and in commercial applications. The innovative solutions offered by this technology in areas such as natural language processing, data analysis, and human-computer interaction have led to the emergence of new research domains across various disciplines. However, there has been no comprehensive examination in the literature of which academic disciplines focus on ChatGPT and which areas have attracted more *** study aims to systematically examine academic publications related to ChatGPT and to identify the disciplines in which this technology is more widely adopted, as well as the areas where intensive research is conducted. To this end, a specially developed tool was utilized to scan all academic publications pertaining to ChatGPT, allowing for a comprehensive interdisciplinary analysis. The results of the analysis reveal the academic fields in which ChatGPT is most intensively utilized, highlighting the differences among these fields. Notably, there is a concentration in categories such as 'Medical Sciences,' 'computer Sciences,' and 'Education Sciences,' which also stand out among the most cited publications. Our findings indicate that ChatGPT has gained broad acceptance within the academic community, with certain disciplines leading the way in this regard. Furthermore, the interactions among categories sharing common keywords demonstrate how ChatGPT facilitates interdisciplinary knowledge flow. © 2024 IEEE.

关键词： Human computer interaction

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Ensuring Electronic Health Record (EHR) Privacy using Zero Knowledge Proofs (ZKP) and Secure Encryption Schemes on Blockchain 5

Ensuring Electronic Health Record (EHR) Privacy using Zero K...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranaweera, T.A.V.Y. Hewage, H.N.H. Hapuhinna, H.K.D.W.M.C.B. Preethilal, K.L.K.T. Senarathne, Amila Ruggahakotuwa, Laneesha Sri Lanka Institute of Information Technology Dept. Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

In the haste of EHR digitization, the protection of patient data becomes paramount, casting a spotlight on small healthcare clinics grappling with the threats of an ever more interconnected healthcare sector. That is due to the existing centralized system and traditional password-based authentications which cause a single point of failure. Besides this patient consent for EHR sharing is narrowed to a one-time consent. To overcome these problems, this study suggests a robust solution that makes data sharing utilizing blockchain while achieving privacy using web-based Zero Knowledge Proofs (ZKP) authentication and Homomorphic Encryption (HE). The research also considers securely outsourcing EHRs to research organizations to improve data exchange for research purposes. Along with patient-controlled encryption, consent control, and time-limited data access, it also explores dynamic consent management while complying with healthcare standards. Lastly, this study stream-lines secure data sharing with insurance providers while ensuring patient privacy and defending against insurance fraud. © 2023 IEEE.

关键词： Blockchain

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Electronic health records protection strategy by using blockchain approach

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Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali D. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping 2

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jhansi Ida, S. Balasubadra, K. Skandarsini, R.R. Lakshmi Narayanaa, T. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India RMD Engineering College Department of Information Technology Thiruvallur India

ISBN: (纸本)9798350365269

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model's training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model's versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model's adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities. © 2024 IEEE.

关键词： Long short-term memory

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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Özbay, Recep Çelebi, Merve Yavanoǧlu, Uraz Gazi University Graduate School of Informatics Dept. Of Computer Forensics Ankara Turkey Hatay Mustafa Kemal University Faculty of Education Dept. Of Computer Education and Instructional Technologies Hatay Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures. © 2024 IEEE.

关键词： Clean energy

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