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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Singh, Shweta Ranganathaswamy, M.K. Renuka, G. Diwakar, Mandar P. Jain, Sandeep Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350319125

Deep learning strategies for content material retrieval have been explored and updated to cope with the want for accurate search effects. This machine up to date is used to date apprehend and examine text-up-to-date content material from sources, up-to-date webpages, or virtual up-todate motion pictures. As the quantity and complexity of online content will increase, so up-to-date have the want for higher up-to-date up to date those resources. Recent advancements in deep gaining knowledge have enabled better content retrieval structures that are up to date, updated correctly, and speedy to understand statistics from one-of-a-kind sources. Such structures include natural language processing (NLP), sentiment analysis, entity reputation, and up-to-date updated reputation. Those deep studying technologies have been used to decorate content material retrieval by enhancing search effects' accuracy, robotically updated extracting applicable statistics, categorizing information, and identifying patterns. Additionally, deep-getting, updated systems are up-to-date and quick to examine large volumes of data, generating and relevant consequences than up-to-date methods. In summary, deep up-to-date techniques can beautify the retrieval system of content material efficiently, enhancing the accuracy and pace of seek effects. © 2023 IEEE.

关键词： categorizing comprehend effectively learning techniques

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Fairness-Aware Optimization of Vehicle-to-Vehicle Interaction for Smart EV Charging Coordination

arXiv

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arXiv 2023年

作者： Khele, Aditya Jiang, Canchen Wang, Hao Faculty of Information Technology Monash University Melbourne Australia Department of Data Science AI Monash University Melbourne Australia Department of Data Science and AI Monash Energy Institute Monash University Melbourne Australia

As the number of electric vehicles (EVs) continues to grow, there is an increasing need for smart charging strategies. This paper exploits the vehicle-to-vehicle (V2V) concept to leverage EVs’ diverse charging patterns and unlock the value of flexibility by enabling energy transfer among EVs. We formulate a cost minimization problem for an EV charging station to optimize the V2V schedule together with vehicle-to-grid (V2G), grid-to-vehicle (G2V) charging, as well as the use of renewable energy. When EVs perform V2V to transfer energy to charge EVs, the traditional cost-minimizing approach may overuse some EVs with lower costs to perform V2V. We address the fairness issue by developing fair V2V energy transfer strategies to avoid the excess discharge from individual EVs. We introduced three kinds of fairness metrics in the V2V optimization problem to demonstrate the fair energy transfers. In addition, we formulate baseline optimization problems without V2G or V2V and compare the results to prove the potency of V2V concept along with V2G method. The simulation results demonstrate that V2V can significantly reduce EV charging costs and highlight the trade-off between fairness enforcement and charging cost minimization. © 2023, CC BY.

关键词： Energy transfer

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Thai voice language encryption and decryption using 3DES and AES algorithm for communication

Thai voice language encryption and decryption using 3DES and...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Dobay, Naruemon Thakong, Mongkhon Somkantha, Krit Somsuk, Kritsanapong Faculty of Science Udon Thani Rajabhat University Department of Data Science and Information Technology Thailand Graduate School Udon Thani Rajabhat University Udon Thani Thailand General Education Udon Thani Rajabhat University Thailand Faculty of Science Udon Thani Rajabhat University Thailand Faculty of Technology and Engineering Udon Thani Rajabhat University Udon Thani Thailand

ISBN: (纸本)9798331543952

The aim of this paper is to compare the performance of the encryption and decryption of Thai voice language by using Triple data Encryption Standard (3DES) and Advanced Encryption Standard (AES) algorithms for secure communication. The research methodology involves collecting Thai voice samples, converting speech to text by using Google's API to be applied with cryptography algorithms. Performance comparison between 3DES and AES focuses on accuracy and processing time. Experimental results demonstrate 100% accuracy in preserving original text content for both algorithms. However, AES consistently outperforms 3DES in processing speed, particularly for larger data sizes. The study reveals a linear relationship between input size and processing time for both algorithms. Furthermore, the findings imply that AES offers superior performance in terms of speed and security for encrypting Thai voice data, making it more suitable for secure communication applications. Therefore, this research contributes to improve data security in Thai voice communications by providing empirical evidence on the efficacy of modern encryption standards. © 2025 IEEE.

关键词： Security of data

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Deep learning for brain age estimation: A systematic review

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information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Optimizing hyperparameters for dual-attention network in lung segmentation

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Intelligence-Based Medicine 2025年 11卷 100221-100221页

作者： Wahyuningrum, Rima Tri Fadillah, Rizki Abdil Yunita, Indah Satoto, Budi Dwi Muntasa, Arif Sari, Amillia Kartika Rahardjo, Paulus Dewi, Deshinta Arrova Bauravindah, Achmad Department of Informatics Engineering Faculty of Engineering University of Trunojoyo Madura Indonesia Department of Health Faculty of Vocational Studies Universitas Airlangga Indonesia Department of Computer Science and Technology Faculty of Data Science and Information Technology INTI International University Malaysia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Indonesia

Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis. © 2025 The Authors

关键词： Chest X-ray images DANet Epoch Hyperparameter Learning rate Lung segmentation Process innovation

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Chernobyl Disaster Optimizer Algorithm for Feature Selection Problems

Chernobyl Disaster Optimizer Algorithm for Feature Selection...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Hisham A. Shehadeh Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid growth of high-dimensional data in Machine Learning (ML) applications presents essential computational challenges. This phenomenon manifests primarily through redundant feature space dimensions, requiring robust dimensionality reduction approaches such as Feature Selection (FS). This research introduces an investigation using the Chernobyl Disaster Optimizer (CDO), a physics-inspired metaheuristic optimization algorithm for solving feature selection problems. We propose a CDO algorithm that utilizes dynamic radiation dispersion mechanisms for wrapper-based feature selection, where solution fitness is evaluated through accuracy metrics obtained from a K-nearest-neighbor (KNN) classifier. Comprehensive empirical analysis across eight UCI repository benchmark datasets shows a satisfactory performance of CDO in classification accuracy and feature selection efficiency with an average of 45.8% compared to competing metaheuristic approaches. Friedman tests, with p-value = 0.0302, confirm the algorithm's significant performance advantages. The results demonstrate that CDO is a reliable optimization algorithm for feature selection tasks, particularly effective in high-dimensional classification problems.

关键词： Dimensionality reduction Measurement Accuracy Heuristic algorithms Disasters Metaheuristics Nearest neighbor methods Feature extraction Classification algorithms Reliability

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Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation

Smart Energy Guardian: A Hybrid Deep Learning Model for Dete...

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IEEE International Smart Cities Conference (ISC2)

作者： Xiaolu Chen Chenghao Huang Yanru Zhang Hao Wang School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu China Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia Shenzhen Institute for Advanced Study Shenzhen China Monash Energy Institute Monash University Melbourne Australia

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

With the proliferation of smart grids, smart cities face growing challenges due to cyber-attacks and sophisticated electricity theft behaviors, particularly in residential photovoltaic (PV) generation systems. Traditional Electricity Theft Detection (ETD) methods often struggle to capture complex temporal dependencies and integrating multi-source data, limiting their effectiveness. In this work, we propose an efficient ETD method that accurately identifies fraudulent behaviors in residential PV generation, thus ensuring the supply-demand balance in smart cities. Our hybrid deep learning model, combining multi-scale Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, excels in capturing both shortterm and long-term temporal dependencies. Additionally, we introduce a data embedding technique that seamlessly integrates time-series data with discrete temperature variables, enhancing detection robustness. Extensive simulation experiments using real-world data validate the effectiveness of our approach, demonstrating significant improvements in the accuracy of detecting sophisticated energy theft activities, thereby contributing to the stability and fairness of energy systems in smart cities.

关键词： Deep learning Temperature distribution Smart cities Power system stability Transformers Stability analysis Robustness Convolutional neural networks Long short term memory Thermal stability

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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H∞Filter-Based Joint RSOP and Phase Noise Tracking in Coherent PDM Systems

H∞Filter-Based Joint RSOP and Phase Noise Tracking in Coher...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Peng, Yawei Liu, Shuai Du, Xinwei Yu, Changyuan BNU-HKBU United International College Faculty of Science and Technology Zhuhai519087 China BNU-HKBU United International College Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science Zhuhai519087 China The Hong Kong Polytechnic University Photonics Research Institute Department of Electronic and Information Engineering Hung Hom Hong Kong

ISBN: (纸本)9781665462136

In this paper, an H∞ filter-based approach is proposed for jointly tracking the rotation of state of polarization (RSOP) and laser phase noise (LPN). The H∞ filter dynamically and robustly estimates the high-dimensional states by limiting the worst-case estimation error, which exhibits excellent tolerance to the noise disturbances. Simulation results verify that the proposed H∞ filter outperforms the conventional extended Kalman filter (EKF) with respect to the tracking accuracy and the tolerance under ultra-fast RSOP. © 2023 IEEE.

关键词： Phase noise

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Privacy-Aware Edge Computation Offloading With Federated Learning in Healthcare Consumer Electronics System

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Yiming Fang, Hao Yan, Zheng Qi, Lianyong Bilal, Muhammad Li, Yang Xu, Xiaolong Zhou, Xiaokang Nanjing University of Information Science and Technology School of Software Nanjing210044 China NanJing University of Information Science and Technology Reading Academy Nanjing210044 China Xidian University School of Cyber Engineering Shaanxi Xi'an710026 China College of computer science and technology Qingdao China Lancaster University School of Computing and Communications United Kingdom Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China Kansai University Faculty of Business and Data Science Japan

The continuous iteration of consumer electronics has significantly promoted the development of medical devices, which has enabled the collection of large amounts of heterogeneous medical data. These data are offloaded from local devices to cloud servers for processing using traditional methods, which results in high transmission latency and the risk of privacy leakage. Additionally, researchers have employed federated learning to protect data privacy, but this approach can lead to a straggler effect due to the limited computational and communication resources of terminal devices. To address these issues, a computation offloading framework is designed to optimize task and resource allocation, addressing multi-optimization problems and mitigating the straggler effect in federated learning. Moreover, a novel computation offloading method within a federated learning framework assisted by edge computing, named DRWB, is proposed. Specifically, we develop a deep reinforcement learning-based approach to transfer lagging training tasks to idle edge servers, enhancing data processing speed, minimizing transmission delays, and protecting user privacy. Extensive experimental results demonstrate that the DRWB method outperforms baseline methods, showcasing superior performance in handling heterogeneous medical data tasks. © 1975-2011 IEEE.

关键词： Electronic health record

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