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9th international conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Deng, Chunwei Shi, Huanqing Guo, Xiaoqi Harbin Petroleum Institute China University of London United Kingdom

ISBN: (纸本)9798400718137

As mobile intelligent terminals, wireless networks and distributed computing technologies mature and spread, more and more data is generated and distributed in the network environment, and the network structure is changing dynamically, the instability of data security is increasing. In order to solve this problem, it is necessary to improve the ability of data security defense in Edge network. Therefore, an optimized Bayesian edge computing intrusion detection algorithm model is established: according to the needs of Edge network characteristics, the current mainstream intrusion detection algorithms are studied and analyzed, RDC curve is used to analyze and compare the two indexes of the common intrusion detection algorithm: pass-true positive rate and false positive rate, the practicability of Bayesian classification algorithm in Edge network intrusion detection is verified by three indexes: accuracy, precision and recall. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Analysis of machine learning Methods for Fault Classification in Electric Systems with Distributed Energy Resources 31

Analysis of Machine Learning Methods for Fault Classificatio...

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31st IEEE international conference on Electronics, Electrical Engineering and computing, INTERCON 2024

作者： Quispe, Juan C. Oliden, Jose Alcantara, Francisco Rosero, Veronica Universidad Tecnológica del Perú Facultad de Ingenieria Lima Peru Instituto de Energía Eléctrica IEE Universidad Nacional de San Juan - CONICET San Juan Argentina

ISBN: (纸本)9798350378344

The continued growth of Distributed Energy Resources (DER) presents challenges to electrical grids, not the least of which is electrical protection. This article develops a classification criterion to identify the fault zone in a distribution network using some well-known machine learning methods on transient RMS signals. The signals considered are: active power, reactive power, voltage, current and frequency extracted from the Power Plant Controller (PPC) of the distribution generator inverter. Then, this information is part of the input for different classification methods to evaluate their efficiency, this by simulating different fault cases. The simulations were performed on a generic model obtained from Power Factory, while the analysis for testing and training was performed in Matlab. The results show that the multilayer perceptron method gives better results. © 2024 IEEE.

关键词： Power distribution faults

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A Study of Race Momentum Based on machine learning Methods and Pearson's Correlation Coefficient 3

A Study of Race Momentum Based on Machine Learning Methods a...

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3rd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Ruofei Hu, Mingjie Wang, Xiaoyang Beihang University Beijing China

ISBN: (纸本)9798331519032

In this study, we developed two models to deeply investigate and predict momentum effects in tennis matches. First, a momentum assessment system model was constructed to categorise momentum effects into past momentum, serving situations, psychological and physical factors, and to identify the effects through data processing and correlation analysis, defining the momentum function and applying it to the 2023 Wimbledon tournament;second, we carried out hypothesis testing to demonstrate the significant effects of momentum on match progression and player scoring. Additionally, we developed the Match Swing Prediction Model, which uses a machine learning approach to predict lead and swing points in a match, providing practical advice to players. By exploring the model's extension to women's tennis and table tennis matches, we found that the momentum effect for women's matches was slightly less significant than for men's matches, but that the model performed well in predicting women's matches. These findings provide useful insights into understanding and predicting momentum changes in tennis matches and provide practical references for players and coaches. © 2024 IEEE.

关键词： Prediction models

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A Comparative Study of Video Watermarking using Various machine learning Algorithms 14

A Comparative Study of Video Watermarking using Various Mach...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Shubham Bansal, Nipun Delhi Technological University New Delhi Department of Computer Science New Delhi India

ISBN: (纸本)9798350335095

Watermarking refers to the process of inserting data or information into your content for the purpose of identification or protection to maintain its authenticity and copyrights. Watermarking is like stenography and is used in many places such as in photos, videos, money, and stamp to assist in identifying counterfeiting. There are many techniques developed for watermarking in which some are wavelet based and some are transform based. Watermarking is a two-way process in which first the watermark is embedded over the data by using embedding techniques such as two-way dimensional discrete wave transform and watermark extraction techniques such as the extreme learning machines, neural networks etc. To provide the clear overview of the existing algorithms in combination with extreme learning machine we went through existing studies from 2014-2022. This review gives an exhaustive study of existing methods and suggests some recommendations for further enhancements to be done by using the various deep learning and machine learning techniques. © 2023 IEEE.

关键词： Watermarking

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Elderly Fall Detection System Using mm-Wave Radar Sensor 4

Elderly Fall Detection System Using mm-Wave Radar Sensor

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Joy, Jeslet John, Amalda Theresa Anna Alex, Angel Nair, Adityakrishna S Sreesh, P.R. Manuel, Anto St Joseph's College of Engineering and Technology Dept. of Electronics and Communication Engineering Palai India

ISBN: (纸本)9798350343670

This project focuses on the implementation of an elderly fall detection system using millimeter-wave radar technology, prioritizing privacy preservation within indoor environments. By harnessing mm-wave radar, our system offers technical advantages over camera-based solutions. Radar operates in the radio frequency spectrum, ensuring privacy, as it does not capture visual data, addressing concerns regarding surveillance and consent. Technical aspects encompass mm-wave radar sensor deployment, signal processing algorithm development for fall detection, and real-time data analysis integration. Radar mitigates issues posed by low lighting, occlusions, and line-of- sight limitations common in camera-based systems. Additionally, machine learning enhances fall detection accuracy, reducing false alarms while maintaining high sensitivity. © 2024 IEEE.

关键词： fall detection machine learning millimeter wave mm-wave Radar sensors Point Cloud radar sensor signal processing

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Predicting MPI Communication for Solving Partial Differential Equations Using machine learning 25th

Predicting MPI Communication for Solving Partial Differenti...

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25th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： Kissami, Imad Kalloubi, Fahd Kiddi, Mohamed Mohammed VI Polytechnic University Lot 990 Hay Moulay Rachid Benguerir43150 Morocco LTI Laboratory Chouaib Doukkali University El Jadida Morocco

ISBN: (纸本)9789819642069

High-performance computing (HPC) is crucial for obtaining solutions to complex scientific and engineering problems described by partial differential equations (PDEs). However, most of these simulations are very sensitive to one major cause of performance bottlenecks: the communication overhead between nodes. While the Message Passing Interface (MPI) is a standard library for communication on HPC systems, the best-suiting MPI communication function is very hard to decide due to numerous variations of algorithms and their dependency on multiple factors. In this paper, we present a machine learning-enabled approach to predict optimal MPI communication functions for large-scale PDE simulations. Our model encompasses many diversified features, like mesh size, number of variables, compiler type, number of cores, and variable types, in its construction to take a more holistic approach toward the optimization of communications. In fact, the model predicts the most suitable MPI function for a given simulation environment by taking into account the aforementioned parameters. Experiment results show clearly that incorporating these features into the prediction model yields the best communication strategy which is highly accurate, reliable, ensuring better scalability and performance. To the best of our knowledge, this is the first thorough study addressing the use of machine learning techniques to optimize MPI communication for PDE simulations;therefore, it is an important contribution for leveraging the predictive capability of machine learning models for the co-design of large-scale numerical simulations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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machine learning-Optimized Blockchain Protocols for Enhanced Data Integrity in Clinical Trials

Machine Learning-Optimized Blockchain Protocols for Enhanced...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Thethi, H. Pal Rajitha, Akula Revathi, V. Prithviraj, Jitendra Yadav, Dinesh Kumar Saud, Haider Radhi Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India GyanGanga Institute of Technology and Sciences DoCSE M.P Jabalpur India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This article reports the findings of a comprehensive investigation that found blockchain technology developed for machine learning can improve clinical trial data security. This research was first submitted to Transactions on Blockchain Technology. Secure Decentralized Data Management System (SDDMS) that successfully addresses data stability, security, and privacy is possible. This is feasible. This makes switching to safer, more transparent medical data processing easy. Blockchain encryption, consensus-driven validation, and federated learning provide the secure and efficient management of patient personal information by the Secure Distributed Database Administration System (SDDMS). As a result, the clinical trial operating environment becomes more trustworthy and transparent. The suggested system has been successfully implemented, suggesting that it has the potential to revolutionize data management, foster collaborative research, and empower individuals to make better and more informed healthcare decisions. Blockchain and machine intelligence are likely to change trustworthy data management in clinical trials and other healthcare research. This is likely. Because this combination is likely to combine the two technologies. © 2024 IEEE.

关键词： Federated learning

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Comprehensive investigation on Deep learning models: Applications, Advantages, and Challenges 15

Comprehensive investigation on Deep learning models: Applica...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Avacharmal, Rajiv Pamulaparthyvenkata, Saigurudatta Pandiya, Dileep Kumar Krishna Prakash, R. Ranjan, Piyush Balakrishnan, Anandaganesh Maheswaran, S. University of Connecticut United States ZoomInfo Technologies United States Department of Information Technology Sona College of Technology Tamil Nadu Salem India IEEE United States Data Engineering American Water United States Department of Electronics and Communication Engineering Kongu Engineering College Perundurai TamilNadu Erode India

ISBN: (纸本)9798350370249

Artificial intelligence (AI) and machine learning (ML) have been completely transformed by deep learning (DL), which provides unmatched power in handling massive, unstructured information from a variety of fields. CNNs, neural networks with recurrent connections (RNNs), generative models, (DRL), and deep learning via transfer are the main deep learning models that are covered in detail in this article. The structure, uses, advantages, and drawbacks of each model are thoroughly investigated. In this work, we use the Fruit-360 dataset, a benchmark picture classification dataset, for an empirical investigation. Six well-known deep learning architectures are compared and evaluated: Bidirectional LSTM, CNN, Simple RNN, (LSTM), (GRU), and Bidirectional GRU. Analysis is done on performance parameters including accuracy, precision, recall, and computing efficiency to have a better understanding of how well they work on various tasks. The purpose of this survey is to give scholars, professionals, and enthusiasts a thorough grasp of the various uses and capacities of deep learning models. The study provides insights that help progress the discipline and direct the selection of suitable models for certain real-world problems. ©2024 IEEE.

关键词： Deep reinforcement learning

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On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2023年 138卷 61-88页

作者： Rahman, Anichur Hasan, Kamrul Kundu, Dipanjali Islam, Md. Jahidul Debnath, Tanoy Band, Shahab S. Kumar, Neeraj Mawlana Bhashani Sci & Technol Univ Dept CSE Tangail Bangladesh Constituent Inst Univ Dhaka Natl Inst Text Engn & Res NITER Dept CSE Savar Dhaka 1350 Bangladesh Hankuk Univ Foreign Studies Dept ICE Seoul South Korea Green Univ Bangladesh Dept CSE 220-D Begum Rokeya Sarani Dhaka 1207 Bangladesh Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ Rd Sect 3 Touliu 64002 Yunlin Taiwan Deemed Univ Thapar Inst Engn & Technol Dept CSE Patiala Punjab India Univ Petr & Energy Studies Sch Comp Sci Dehra Dun Uttarakhand India Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan King Abdulaziz Univ Jeddah Saudi Arabia

The individual and integration use of the Internet of Things (IoT), Information-Centric Networking (ICN), and Federated learning (FL) have recently been used in several network-related scenarios and have consequently experienced a growing interest in the research community. Federated learning addresses the privacy and security issues of the IoT data in a decentralized manner. Also, it can be capable of training the multiple learning algorithms through local content except for exchanging data through intelligent Artificial Intelligence (AI)-based algorithms. Moreover, in ICN, the content is retrieved and stored based on the content name rather than the content location address. On the other hand, it is challenging to support the massive IoT devices by the fifth generation (5G) mobile-cellular networks. Therefore, the cellular 6G networks are expected to increase the connection capabilities by 10-100 times over 5G, which necessitates a convergence of Communication, computing, and Caching (3C). At the same time, the in-network caching capabilities of ICN can be attractive features for IoT networks. IoT aspires to link anybody and/or everything at any time and location. However, integrating IoT with different areas is a new academic topic and is still in its infancy. As a result, this research highlights the potential of ICN for IoTs by conducting an exhaustive literature review. This work provides a comprehensive survey regarding these three recent research trends (i.e., FL, IoT, and ICN) and reviews the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to take advantage efficiently. Indeed, we consider their group-wise (FL-ICN-IoT) utilization based on the need for more robust security and privacy. Additionally, we cover the application fields of these technologies both individually and combinedly. Finally, we discuss

关键词： Internet of Things Information-Centric Networking Federated learning Communication Security computing machine learning Privacy Artificial Intelligence Confidentiality

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DEVELOPMENT AND EVALUATION OF A METHOD FOR IDENTIFYING LOGIC ERRORS USING machine learning WITH A FOCUS ON PROGRAM STRUCTURE 21

DEVELOPMENT AND EVALUATION OF A METHOD FOR IDENTIFYING LOGIC...

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21st international conference on Applied computing 2024, AC 2024 and 23rd international conference on WWW/Internet 2024, ICWI 2024

作者： Harada, Yuta Sato, Soichiro Nakamura, Shoichi Miyadera, Youzou Department of Education Tokyo Gakugei University 4-1-1 Nukuikitamachi Koganei-shi Tokyo184-8501 Japan Department of Computer Science and Mathematics Fukushima University 1 Kanayagawa Fukusima-shi Fukushima960-1296 Japan Division of Natural Science Tokyo Gakugei University 4-1-1 Nukuikitamachi Koganei-shi Tokyo184-8501 Japan

ISBN: (纸本)9789898704627

In programming exercise classes at universities, instructors must quickly determine the learner's situation and provide support. However, it is difficult to deal with learners making logic errors. This is because there is more than one way to solve a task that a learner is working on, and the method of program logic coding (hereinafter, "coding method") differs for each student. Therefore, when instructors help learners solve logic errors, they must be supported by considering each learner's coding method. This study aims to develop a method for identifying logic errors that learners are currently making, considering each learner's coding method. In this study, we assume that the learner’s coding method is reflected in the program structure. We then attempt to analyze the program focusing on its structure. Specifically, using clustering, we tried to categorize source code by the learner’s coding method and identify logic errors in it. Results of evaluating our method using the Hold-out Validation show it can identify logic errors with 70% accuracy. This result indicates a new approach for instructors to help learners when they are stuck during programming practice. © 2024 Proceedings of the international conferences on Applied computing and WWW/Internet 2024. All rights reserved.

关键词： Identifying Stumbling Logic Error machine learning Programming learning Support Source Code Editing History

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