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Journal of Network Intelligence 2024年第4期9卷 2084-2098页

作者： Huang, Xin-Yu Liang, Quan Chen, Xian-Fu Zhan, Chuang-Tao School of Electronic Electrical Engineering Physics Fujian University of Technology Fuzhou350118 China School of Computer Science Mathematics Fujian University of Technology Fuzhou350118 China

With the continuous construction of HVDC, it has gradually become dis-tances and large capacity transmission, as well as one of the main technologies of regional power grid interconnection. The scale of AC-DC hybrid grid continues to grow, bringing new challenges to reactive power balance and voltage stability of power grid. An improved whale optimization algorithm is proposed to solve the reactive power optimization problem of AC-DC hybrid systems. By introducing local neighborhood search mechanism, adaptive threshold and adaptive inertia weight, the local search accuracy, global search ability and convergence speed of the algorithm are improved. The reactive power optimization model is established with the total network loss and node voltage offset as the objective function, and the solution steps are given. An IEEE 22-node system with DC transmission lines is used as an example to simulate the proposed algorithm, and compared with the traditional whale optimization algorithm and genetic algorithm, the results show that the proposed algorithm has good convergence and stability, and can obtain better reactive power optimization effect. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Optimization algorithms

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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Automatic Pill Dispenser for Elderly Medical Adherence: A Survey 5

Automatic Pill Dispenser for Elderly Medical Adherence: A Su...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Divya, K.S. Ragurja, Ashwin Devanand, Gayathri Thomas, Antony Mannickathan, Gripsy Paul Nataraj, Neetha K. Adi Shankara Institute of Engineering and Technology Department of Computer Science and Engineering Kerala Kalady India

ISBN: (纸本)9798350383522

Automatic Pill Dispensers are becoming a necessity in the current ages. The survey paper mainly examines the contemporary medicine pill dispensers focusing specifically on Internet of Things (IoT) usage and its features. This meets the needs of elderly individuals with poor memory, making it suitable for use in nuclear nursing homes and shelters. The survey goes in depth into the different applications and systems used at these dispensers and reviews historical data to show the evolution of technology in this area. The main goal is to identify and investigate the unique features associated with each method. Additionally, the paper highlights the shortcomings in these systems and provides a better understanding of the progress and limitations of medicine dispensers designed for elderly. © 2024 IEEE.

关键词： Authentication Elderly Healthcare IoT Pill Dispenser

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Block chain-based trusted smart contract for secure mobile user payment gateway in e-health systems

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Soft Computing 2024年第17-18期28卷 10139-10151页

作者： Saranya, A. Naresh, R. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India

In other instances, the cloud transfers payment information directly to the merchant’s server without first doing any fraud checks. Block authentication between the cloud and a healthcare merchant server is the goal of this study, which is designed to prevent the deployment of fraudulent servers. Smart contract verification on the blockchain will be used to this end. The cloud assault must be prevented from accessing personal payment and card information. It is via the employment of cryptography methods that this concept of privacy reservation is made possible. The Boolean crossover depth first search (DFS) technique is used to encrypt data in the payment gateway protocol at its inception. When the hospital administrator solicits card data from patients or users, our innovative approach swiftly initiates data encryption using the DFS technique. This method meticulously traverses the data structure in a depth-first manner, employing Boolean operations to systematically encrypt sensitive information. Through this process, the patient's payment and card data undergo secure transformation, rendering it unreadable to unauthorized entities. Additionally, an authentication key is generated concurrently with the encryption process, enabling verification between the system and the user before any data transmission occurs. The integration of the DFS technique serves as a fundamental layer of defense against potential cloud-based attacks. Its application ensures the utmost protection of personal payment and card details throughout the transaction process, bolstering the security infrastructure of our proposed system. A comprehensive analysis comparing our approach with existing methods showcases the efficiency and reliability of our proposed system in terms of Execution Time (ms), Encryption Time (ms), Decryption Time (ms), and Memory (bits). This study aims to bridge the gap in transactional security, ensuring robust protection against unauthorized access and data breaches in

关键词： Smart contract

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Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations

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International Journal of Fuzzy Systems 2024年第8期26卷 2536-2552页

作者： Haobijam, Basanta Huang, Yo-Ping Chang, Yue-Shan Chang, Tsun-Wei Department of Electrical Engineering National Taipei University New Taipei City23741 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science and Information Engineering HungKuo Delin University of Technology New Taipei City236302 Taiwan

Pisciculture encounters an array of intricate challenges that span disease management, preservation of water quality, prevention of genetic hybridization, ensuring the integrity of net systems, sourcing sustainable aquatic feed, and comprehending fish growth and reproductive dynamics. Addressing these multifaceted challenges necessitates a comprehensive research approach. This study employs an innovative synergy of fuzzy logic and deep learning techniques, resulting in a robust strategy to tackle these obstacles effectively. Fuzzy logic excels in assessing stressed fish conditions by handling inherent uncertainties. Simultaneously, YOLOv7 with fuzzy color enhancement (YOLOv7FCE) is used to detect damaged fish nets, thereby mitigating losses and upholding the integrity of the net infrastructure. This approach also leverages YOLOv7FCE for identifying Cobia fish within shoals, streamlining the identification process. Subsequently, DeepLabv3 is implemented to meticulously segment the recognized Cobia fish, facilitating precise measurements of their physical attributes. This comprehensive methodology yields profound insights into growth patterns and feeding tendencies within the confined aquatic environment. By embracing this approach, the research presents a versatile and adaptive framework that not only enhances our comprehension of piscine dynamics but also holds the potential to revolutionize the aquaculture industry. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fish

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Learning from Subgraphs and Embeddings for Sign Prediction 21

Learning from Subgraphs and Embeddings for Sign Prediction

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Wan, Shuyan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

With the development of internet, the existence of signed networks has become increasingly common. These networks contain signed information in links, such as friendly and hostile relationships in social networks. Sign prediction is an important task on signed networks, and many methods have been proposed to address it, especially signed graph representation learning. There are two classical social theories on signed networks: Balance theory and status theory. Most previous signed graph representation learning methods have been based on balance theory, while often overlooking the importance of status theory. Furthermore, in sign prediction, models have typically focused only on the two nodes related to target link, failing to fully leverage the information contained in the triadic relationships between nodes. This paper proposes a multi-head attention-based signed graph representation learning method that incorporates both balance theory and status theory, and extracts local subgraphs of target links to capture triadic relationship information between nodes for sign prediction. Experimental results on several real-world signed network datasets for sign prediction tasks demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Prediction models

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Ensemble Based Precautionary Detection of Parkinson's Disease from Voice Signal 1

Ensemble Based Precautionary Detection of Parkinson's Diseas...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Bhanusree, Yalamanchili Adhvy, Vodnala Sharan Affan, Mohammed Shaji Praneetha, Kanvapuri Sai Sahithi, Katikireddy Sai Computer Science - Data Science VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India

ISBN: (纸本)9798331518943

Parkinson's Disease is a neurological progressive disorder that affects the nervous system of a human being, which effects the parts of the body. Tremors, imbalance, slowed movement, stiffness, difficulty in walking and talking etc. are observed. The reasons being, brain disorder, injuries, toxins certain drugs etc. The disease has no cure however treatment can be offered for relief from symptoms. However early detection can cure the disease in the first stages and extend the life span of the patient. This paper proposes a Ensembled learning model for early detection of Parkinsons disease through voice features. Random forest ensemble models have outperformed the Machine learning and Neural Network models in detecting disease from voice. An accuracy of 94.8% is achieved with this model with proper preprocessing techniques and training. As a future work the proposed model's software prototype can be released for mobile application for cost effective early detection of Parkinsons Disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Harnessing Ridge Regression and SHAP for Predicting Student Grades: An Approach Towards Explainable AI in Education 3rd

Harnessing Ridge Regression and SHAP for Predicting Student ...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Katkar, Vijay Kadam, Swapnil Mulla, Juber Nadaf, Niyaj Department of Computer Science and Engineering Annasaheb Dange College of Engineering and Technology Maharashtra Sangli India

ISBN: (纸本)9789819773558

This paper presents a comparative analysis of several regression techniques for predicting student academic performance, with a particular focus on Ridge Regression and its interpretability through SHAP, a method of Explainable Artificial Intelligence (XAI). The models evaluated include Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Decision Tree Regression, Random Forest Regression, AdaBoost, Gradient Boosting, Bagging, XGBoost, and K-Nearest Neighbors. Using a dataset encompassing student family background, personal information, and recent academic grades, we trained and evaluated these models to predict future academic performance. Experimental results demonstrated that Ridge Regression outperformed all other models in predictive accuracy with the highest r2 score of 0.9036 and lowest RMSE of 1.4623. Furthermore, we employed SHAP values to interpret the predictions made by the Ridge Regression model, revealing key feature contributions to the model’s output. This research underscores the potential of Ridge Regression and SHAP in building predictive and interpretable models in the educational domain. This fusion of predictive accuracy and interpretability provides invaluable insights for educators and policy-makers alike in understanding and enhancing student academic achievement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Designing a Graphics Processing Unit for 2D Rendering on FPGA for Educational Purpose 3

Designing a Graphics Processing Unit for 2D Rendering on FPG...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Saha, Rupak Sadad, Nahin Ul Mondal, Md. Nazrul Islam Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

In this paper, design and implementation of a 2D graphics processing hardware unit using FPGA for educational purpose is presented. We propose a more simple & minimalist GPU architecture, which is flexible and easy to understand than existing industrial GPUs. We present a platform for understanding GPU architecture by implementing of graphics algorithms on FPGA for students to use this in laboratories. Students can be benefited from this work as it can be used as a base understanding tool in terms of GPU architecture. It can also be extended to be used in cross disciplinary applications like 3D rendering, computer vision and signal processing visualisation. This research includes descriptions of hardware realization of 2D rasterization rendering algorithms. Hardware Description Language (HDL) called verilog is utilized in designing the architecture. Xilinx Vivado Design Suite software is used for FPGA implementation, targeting Artix 7 FPGA board, which allows users to comprehend the fundamental ideas of hardware design and their applications in graphics rendering. © 2024 IEEE.

关键词： Graphics processing unit

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