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2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Duan, Xuesong Zhang, Jinjia Li, Yuhao Li, Hongchao Wang, Wei State Grid Henan Electric Power Company State Grid Zhenping County Power Supply Company 474250 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Traditional battery maintenance methods have some problems, such as low efficiency, difficult to find potential problems in time and accurately, which affect the stability and reliability of power system. This paper studies the application and optimization of networked battery maintenance mode in power system. By integrating sensor technology, network communication and data analysis, this mode realizes real-time monitoring and maintenance automation of battery state. Aiming at the challenges of lack of real-time and accuracy, optimization strategies such as deploying 5G base stations, adopting advanced algorithms and machine learning technologies are proposed. In addition, a big data analysis platform based on cloud computing is built, and machine learning and data mining are used to predict battery performance decline, and intelligent maintenance algorithms are developed. Experiments show that this mode improves voltage stability, capacity retention and internal resistance control, reduces maintenance cost and improves efficiency. These achievements provide theoretical and practical guidance for the stable operation of power system and efficient maintenance of battery. © 2025 SPIE.

关键词： Batteries Data transmission Resistance Data analysis Data modeling Machine learning Telecommunications Cloud computing Decision making Reliability

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advanced Machine Learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

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3rd international conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： power transmission lines

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Enhancing multi-omics data classification with relative expression analysis and decision trees

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JOURNAL OF COMPUTATIONAL SCIENCE 2025年 84卷

作者： Czajkowski, Marcin Jurczuk, Krzysztof Kretowski, Marek Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

This study introduces the Relative Multi-test Classification Tree (RMCT), a novel classification method tailored for multi-omics data analysis. The RMCT method combines the interpretative power of decision trees with the analytical precision of Relative eXpression Analysis (RXA) to address the complex task of examining biomedical data derived from diverse high-throughput technologies. The proposed RMCT approach discerns patterns within and across omics layers, yielding an accurate and interpretable classifier. In each internal node of RMCT, we create a multitest- group of Top-Scoring-Pair tests, that capture the ordering relationships among features from various omics. Multi-tests are optimized for maximal reduction of Gini impurity, and ensuring consistency in decision-making. We address computational challenges by advanced GPU parallelization, remarkably improving RMCT's time performance. Through experimental validation on diverse multi-omics datasets, RMCT has demonstrated superior performance compared to traditional tree-based solutions, particularly in terms of accuracy and clarity of predictions. This method effectively reveals intricate interactions and relationships within multi-omics data, marking it as a useful addition to bioinformatics and biomedicine. This work represents a thorough extension of our preliminary research, which was initially presented at the twenty-third edition of the international conference on Computational Science (ICCS). It expands the initial concept of integrating decision trees with RXA for multi-omics data classification, deepening the analytical methodologies, further optimizing the GPU computing, and broadening the experimental validation.

关键词： Multi-omics data Decision trees Relative expression analysis Classification GPU computing

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Harnessing the power of AI with Deep Learning: innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Review on Deep Learning-Based Pansharpening Approaches 4th

A Comprehensive Review on Deep Learning-Based Pansharpening ...

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4th international conference on Smart IoT Systems: innovations and computing, SSIC 2023

作者： Kaur, Gurpreet Malhotra, Manisha Singh, Dilbag University Institute of Computing Chandigarh University Mohali India Center of Biomedical Imaging Department of Radiology New York University Grossman School of Medicine New YorkNY United States

ISBN: (纸本)9789819751990

Pansharpening involves merging a high-resolution panchromatic image with a low-resolution multispectral image to create a fused image that is more informative for remote sensing applications. Various techniques have been developed to improve fusion results by leveraging the spatial and spectral characteristics of the input images. In recent years, deep learning has significantly advanced pansharpening, offering promising results through its strong feature extraction and reconstruction capabilities. However, there is a lack of comprehensive analysis of the latest deep learning techniques across different fusion applications. This paper provides a review of various Pansharpening methods employing deep learning, comparing them based on optimal parameters. It also discusses the limitations, challenges, issues, and future directions in Pansharpening approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： CNNs Deep learning Pansharpening

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A Confidentiality-Preserving Distributed Linear Programming Model for Solving Large-Scale Economic Dispatch Problems 24

A Confidentiality-Preserving Distributed Linear Programming ...

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11th international conference on Networking, Systems and Security, NSysS 2024

作者： Hossain, Md Turab Hossain, Md. Alamin Adnan, Muhammad Abdullah Dhaka Bangladesh

ISBN: (纸本)9798400711589

The primary goal of the economic dispatch (ED) is to organize the generation schedule to fulfill system energy demand at the lowest possible operational cost while meeting all other requirements, including system security and reliability. The inclusion of smart grid technologies requires advanced computational techniques and increased problem-solving capabilities to manage effectively. The in-house computing infrastructure has drawbacks, such as high costs, operational complexity, limited flexibility, and security risks. Even though cloud computing is a substitute, it faces the major challenge of storing sensitive data, such as power system data, in the cloud, which could put organizations at risk of cyberattacks. We propose an outsourcing model of distributed linear programming (LP) that solves large-scale economic dispatch problems. Moreover, for the preservation of data confidentiality, we transform our LP into a confidentiality-preserving LP. We have shown the efficacy of our model by implementing on the real-life Bangladesh power grid system. © 2024 Copyright held by the owner/author(s).

关键词： Smart power grids

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601387

The proceedings contain 49 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: Nepali Word Spelling Correction Using Ensemble Learning Technique;the Impact of Information Security Policies and innovations in Digital Technology on the Transformation of Healthcare in Developing Nations: A Literature Review;integrating Multi-omics and Clinical Narratives for Predictive Modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis;a Comprehensive Survey of Fake Review Detection Technology;a Hybrid Intelligent Decision Support System for Automating Financial Credit Evaluation;Advancing Taxation in the New Era: Enhancing Tax Ratios with the Core Tax Administration System (CTAS);Enabling Grid Stability: Harnessing μPMU Data for Data-Driven Analysis of Grid Frequency Events;machine Learning Techniques to Detect Fake News on Social Media: A Systematic Review;cutting-Edge Technology-Based Social Enterprises in India for Sustainable, Inclusive Healthcare;enhancing Telugu Sarcasm Classification Models with Word Embeddings in Imbalanced Datasets;capital Punishment: Analyzing Trends in the United States (1976–2016);exploring the Technostress Issue Among Indonesian Young Entrepreneurs;Teaching Computer Science Using Cloud-Based IDE with Perspectives in Inclusivity;Comparison of Data Encryption Standard (DES) and advanced Encryption Standard (AES) in Security Issues of Cloud computing: A Literature Review;enhancing Corporate and Factory Training Through Game Development: A Comparative Review;usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review;performance Efficiency of Cloud computing—A Literature Review;decentralized Finance (DeFi) Wallets: A Review of its Efficiency, Usability, and Effectiveness;Social Networks on WEB 3.0.

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601424

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601462

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Review on advanced computing Control Techniques in BLDC Motors for Electrical Vehicles 3

Review on Advanced Computing Control Techniques in BLDC Moto...

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3rd international conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2025

作者： Vara Prasad, K.S.R. Usha Reddy, V. Department of EEE S.V.U College of Engineering Tirupati India

ISBN: (纸本)9798331527549

Among all electrified transportation instruments, electric vehicles (EVs) garnered high mark lately and are becoming increasingly popular trends because they can both vie with and surpass vehicles powered by fossil fuel. Enhancing range of vehicle driving and minimizing energy utilization are the main goals of EV advancement. These are necessary goals that can only be met by enhancing and advancing motor performance. With the peak power density and capabilities compared with other motors, brushless direct current (BLDC) motor is best option for electric vehicles (EVs). In addition to their excellent performance in position and speed controls, BLDCs are becoming more and more common in EVs because they can smoothly maintain torque and reduce torque ripple. An overview of several advanced control strategies is given in this study, it also discusses the different fault that occur in BLDC motors and their mitigation strategies. In addition, current issues and challenges in developing torque ripple mitigation management systems are discussed, along with comparisons and recommendations for further research on electric vehicle technology. Lastly, the importance and possible consequences of BLDC motor drive in EVs with various control schemes are revealed via evaluation research. © 2025 IEEE.

关键词： Brushless DC motors

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