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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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Fast and Secure Coding for Wireless Networks

Fast and Secure Coding for Wireless Networks

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International Conference on Computing and Networking technology (ICCNT)

作者： Seema Sambargi Y.V. Dongre N. V. Balaji Saniya Khurana Sowmiya S Sidhant Das School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka comp Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Speedy and comfy coding for wireless networks is an essential problem in the trendy computing landscape. Wi-fi networks have become increasingly commonplace in recent years, and they present specific demanding situations for code builders. To ensure certain cozy, reliable, and efficient overall performance of network applications, developers must be able to quickly and appropriately generate code for wireless structures. The way to meet these requirements, builders have to be capable of selecting the most appropriate coding approaches to apply if they want to limit errors and maximize security. The main venture to developing easy and reliable code for wireless structures is the complexity of the underlying structures. The ever-increasing complexity of underlying community technologies renders conventional coding techniques inadequate. The elevated demands for real-time records and connection transmission exacerbate this. To address this venture, rapid and at-ease coding strategies need to be carried out. Those strategies can encompass, however are not constrained to, principles including comfy coding, exceptional coding practices, and diverse safety protocols. Principals of comfy coding confer with a method that emphasizes comfortable practices at some point in the improvement and protection of the codebase. This consists of proper protection practices along with authentication, encryption, enter validation, error dealing, and malicious code prevention. This technique allows make certain that the code is written in a strong, comfortable, and efficient manner.

关键词： Codes Protocols Wireless networks Encoding Complexity theory Security Reliability Communication system security Protection Wireless fidelity

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Ambipolarity suppression in electrically doped tunnel field effect transistor using asymmetric bias in polarity gates

e-Prime - Advances in Electrical Engineering, Electronics an...

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2025年 11卷

作者： Bhat, Akshatha Awadhiya, Bhaskar Yadav, Sameer Nanjappa, Yashwanth Pahuja, Abhishek Agrawal, Sachin Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Computer Science Engineering Gyan Ganga Institute of Technology and Sciences M.P. Jabalpur 482003 India Department of Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005 India Department of Electronics and Communication Engineering National Institute of Technology Delhi India

In this article, we have addressed an important issue called ambipolarity in electrically doped tunnel field effect transistors, which greatly hinders its use in circuit applications. We introduced asymmetric biasing in polarity gates 1 and 2 of the electrically doped TFET. Polarity gate 2 has a fixed bias of -1.2 V and polarity gate 1 is provided with a variable bias of +1.2 V, +1.0 V, and +0 .8V. The asymmetric bias in both polarity gates introduces asymmetric doping in the source and drain regions, which introduces a wider tunneling width in the drain-channel region and prevents the tunneling of holes;hence, the ambipolar current is suppressed. The structure is studied in the thermal equilibrium, OFF, ON, and ambipolar states. © 2025

关键词： Ambipolarity Boltzmann limit Electrically doped TFET

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Detection of Potato Plant Diseases Using Deep Learning

Detection of Potato Plant Diseases Using Deep Learning

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Kholiya Arnav Pandey Neeraj Kumar Pandey Ankur Dumka Amit Kumar Mishra Piyush Paliwal School of Agriculture Graphic Era Hill University Dehradun India St. Joseph's Academy Dehradun India Department of CSE Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Women Intitute of Technology Dehradun India Uttarakhand India Computer Science & Engg. Graphic Era Hill University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

There is a very famous medical quote “The good- news stories in medicine are early detection, early intervention”. This applies very well in the field of agriculture as well. According to a report published in Research gate form India faces a total loss of INR 290 billion per annum. Potato crop is among the most important crop in the world. Potato crops are important because of many factors which may include benefits like Dietary Diversity, Calorific Efficiency, wide Global Acceptance etc. There are total of 30,000 plant diseases out of which 5,000 prevail in India. Potato disease such as Early Blight and Late Blight are among the diseases that leads to a huge potato crop loss in the whole world as well as India. This paper focuses on using technological advancement in the field of computer science as well as camera hardware capabilities to detect such plant diseases at the earliest stage which will help farmers to take appropriate measures. Some of the technologies that can be incorporated into agricultural field to detect potato plant diseases are CNN (Convolutional Neural Network), ANN (Artificial Neural Network), RNN (Recurrent Neural Network) etc.

关键词： Deep learning Plant diseases Recurrent neural networks Plants (biology) Crops Agriculture Hardware Sensors Convolutional neural networks Unsupervised learning

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Detection of a Novel Object-Detection-Based Cheat Tool for First-Person Shooter Games Using Machine Learning

Detection of a Novel Object-Detection-Based Cheat Tool for F...

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International Conference on Software engineering Research, Management and Applications (SERA)

作者： Zhang Xiao Takaaki Goto Partha Ghosh Tadaaki Kirishima Kensei Tsuchida Graduate School of Information Sciences and Arts Toyo University Saitama Japan Faculty of Information Sciences and Arts Toyo University Saitama Japan Department of Computer Science and Engineering Academy of Technology West Bengal India

Detection of novel game cheating tools is critical for ensuring fair online play. Such cheating tools are visual-based and effectively avoid detection because they do not change the data of game software. With the development and popularity of artificial intelligence technology, it has become easier for individuals to develop cheating tools, such as a new cheating tool for first-person shooter games that searches for characters on the game screen and automatically targets them. Therefore, in this study, a new cheat detection method is proposed using machine learning. The proposed method can be used to detect new cheating tools based on object detection.

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Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Databases

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Dat...

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International Conference on Data engineering

作者： Rui Qiu Yi Ming Yisen Hong Haoyu Li Tong Yang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Academy for Advanced Interdisciplinary Studies Peking University Peng Cheng Laboratory Shenzhen China

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of *** design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. Theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases.

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Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking

arXiv

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arXiv 2025年

作者： Sadeghi, Alireza Hajati, Farshid Argha, Ahmadreza Lovell, Nigel H. Yang, Min Alinejad-Rokny, Hamid Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC United States School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2350 Australia School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Sydney SydneyNSW2052 Australia

In healthcare, integrating diverse biomedical data modalities is critical for comprehensive analyses that can lead to better patient outcomes. Graph-based models have emerged as effective tools for handling such complex, non-Euclidean data by leveraging spatial and relational structures. However, model interpretability remains crucial for their successful deployment and for regulatory-approved implementations in clinical settings, as healthcare practitioners need transparent insights to build trust and guide decision-making. In this work, we present a technical and comprehensive review of research on interpretable graph-based models applied to multimodal biomedical data, covering studies from January 2019 to September 2024. Our analysis of published papers reveals a dominant focus on disease classification (particularly cancer) and a heavy reliance on static graph construction methods (e.g., Pearson correlation, Euclidean distance). While some approaches incorporate inherently interpretable designs, most rely on post-hoc techniques originally developed for non-graph data—such as gradient-based saliency or SHAP (Shapley additive explanations)—and only a small fraction utilize graph-specific methods like GNNExplainer. To address these gaps, we categorize existing explainable artificial intelligence (XAI) approaches according to their interpretability strategies and highlight evolving trends, including the graph-in-graph paradigm, the integration of knowledge graphs, and dynamic edge construction. To illuminate their practical implications, we compare and benchmark representative XAI techniques (sensitivity analysis, gradient saliency, SHAP, and graph masking) using a real-world Alzheimer’s disease (AD) dataset. Our findings show that while SHAP and sensitivity analysis reveal a broader spectrum of established disease-related pathways and Gene Ontology terms, gradient saliency and graph masking highlight unique metabolic and transport processes, underscoring the comple

关键词： Graph neural networks

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Comparing the Effectiveness of Generative Adversarial Networks for Clustering Tasks

Comparing the Effectiveness of Generative Adversarial Networ...

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International computer sciences and Information Technologies (CSIT)

作者： Volodymyr Lytvynenko Serge Olszewski Volodymyr Osypenko Iryna Lurie Violeta Demchenko Dariia Dontsova Department of Informatics and Computer Science Kherson National Technical University Kherson Ukraine Department of Radio Engineering and Electronic Systems Taras Shevchenko National University Kyiv Ukraine Department of Computer Science Kyiv National University of Technology and Design Kyiv Ukraine State Institution «Kundiiev Institute of Occupational Health of the National Academy of Medical Sciences of Ukraine» Kyiv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silhouette Score, Davies-Bouldin Index, Adjusted Rand Index, and Mutual Information. WGANs demonstrated the best clustering performance, while GANs showed promising results but were outperformed by WGAN. Contrary to expectations, WGAN-GP performed the worst. The research underscores the potential of GANs in clustering and highlights the need for further optimization based on specific datasets.

关键词： Training Measurement Iris Stability criteria computer architecture Generative adversarial networks Indexes Information technology Mutual information Optimization

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Reliability calculations based on an enhanced transformer life expectancy model

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Ain Shams engineering Journal 2022年第4期13卷

作者： Ibrahim, K. Sharkawy, R.M. Temraz, H.K. Salama, M.M.A. Electrical and Control Engineering Department Arab Academy for Science and Technology Cairo Egypt Electrical Power and Machines Engineering Department Faculty of Engineering-Ain Shams University Cairo Egypt Electrical and Computer Engineering Department University of Waterloo Waterloo Canada

In this paper, The IEEE thermal ageing model is utilized to build an enhanced Transformer Life Expectancy (TLE) model. The enhanced model is developed relying on the current transformer Health Index (HI) in addition to the loading conditions. A direct correlation between the IEEE and the (HI) ageing factors is identified and projected. The model is capable of integrating factors affecting transformers. Transformer faults and maintenance are amongst factors taken into consideration to improve the model. The reliability calculations are applied for assessing the transformers as well as the distribution network. The calculated transformer (HI) score with the aid of historical failure rate are utilized during the calculation of the distribution network reliability. An application on a simple network connectivity including transformers and loads is demonstrated to verify the strategy effectiveness. The results of this research are the basis for an enhancement for the smart grids performance estimation. © 2021 THE AUTHORS

关键词： Thermal aging

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Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Chang, Yuchao Chen, Wen Li, Jun Liu, Jianpo Wei, Haoran Wang, Zhendong Al-Dhahir, Naofal The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Electronic and Optical Engineering Nanjing University of Science Technology Nanjing210094 China Science and Technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai201800 China Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaboratively managing multi-BSs' radiated power levels with constraints on the users' traffic volume and achievable rate. Considering stochastic traffic arrivals at the users and time-varying network interference, we first formulate the problem as a Markov decision process (MDP) and then develop a novel deep reinforcement learning (DRL) framework based on the cloud-RAN operation scheme. To tackle the trade-off between complexity and performance, the overall optimization of multi-BSs' energy efficiency with the multiplicative complexity constraint is modeled to achieve nearoptimal performance by using a deep Q-network (DQN). In DQN, each BS first maximizes its individual energy efficiency, and then cooperates with other BSs to maximize the overall multi-BSs' energy efficiency. Simulation results demonstrate that the proposed algorithm can converge faster and enjoy a network energy efficiency improvement by 5% and 10% compared with the benchmarks of the Q-learning and sleep schemes, respectively. © 2023, CC0.

关键词： Reinforcement learning

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