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FLSN-MVO: Edge Computing and Privacy Protection Based on Federated Learning Siamese Network With Multi-Verse Optimization Algorithm for Industry 5.0

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3443-3458页

作者： Yin, Shoulin Li, Hang Laghari, Asif Ali Teng, Lin Reddy Gadekallu, Thippa Almadhor, Ahmad Shenyang Normal University Software College Shenyang110034 China Harbin Engineering University School of Information and Communication Engineering Harbin150000 China Lovely Professional University Division of Research and Development Phagwara144411 India Chitkara University Center of Research Impact and Outcome Rajpura140401 India Al Jouf University College of Computer and Information Sciences Department of Computer Engineering and Networks Sakaka72388 Saudi Arabia

With the development of deep learning technology, artificial intelligence has important applications in all aspects of society, but the lack of data has become a vital factor restricting the further evolution of artificial intelligence in industry 5.0. Federated learning can effectively use the edge node data and solve the data problem of artificial intelligence model training by sharing gradient. In federated learning, the terminal transmits the updated model parameter values instead of the primordial data to the server, thus becoming a key technology to ensure data privacy and security in edge computing. However, since attackers can use the shared gradient to launch malicious attacks to steal users’ privacy, how to securely upload the gradient and aggregate it has become an important topic to ensure privacy security in federated learning. Therefore, this paper proposes an edge computing and privacy protection based on federated learning Siamese network with multi-verse optimization algorithm for industry 5.0. This scheme can reduce the expenditure of endpoint participation in federated learning while protecting user privacy. In the federated learning structure, the feature information of the input samples is mapped to a new output vector through the subnetwork of the Siamese network, and the approximation degree between the input samples is judged by comparing the similarity degree between the output vectors. The parameters of the network are optimized by multi-verse optimization algorithm to reduce the convergence time. Meanwhile, an adaptive weight aggregation algorithm is designed to reduce the degradation of model performance and stability caused by data quality differences, so as to improve the accuracy of the model and accelerate the model to reach the optimal value. Finally, comprehensive experiments on three public standard data sets show that the proposed method achieves higher model accuracy and faster model convergence than the most advanced methods. ©

关键词： Federated learning

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer Science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Enhancing the Efficiency of the Grouping-Scoring-Modeling Framework with Statistical Pre-Scoring Component for Transcriptomic Data Analysis

Enhancing the Efficiency of the Grouping-Scoring-Modeling Fr...

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18th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Khokhar, Maham Bakir-Gungor, Burcu Yousef, Malik Department of Data Science Social Sciences Institute Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat13206 Israel

The advent of high-throughput transcriptomic technologies has generated vast transcriptomic datasets, challenging current analytical methodologies with their sheer volume and complexity. The Grouping-Scoring Modeling (G-S-M) approach is one of the recent approaches that treat groups of genes (or clusters of genes) by embedding prior biological knowledge with machine learning in order to detect the most significant groups for classification tasks. The G-S-M might need to treat thousand ten thousand of groups (scoring those groups) which might affect the speed and performance of the algorithm. In response, this study introduces the Pre Scoring G-S-M model, an enhancement of the established Grouping-Scoring-Modeling (G-S-M) framework. This approach incorporates a Pre-Scoring component that leverages the Limma package for its empirical Bayes methods to optimize initial transcriptomic data evaluation through a percentage-based selection of statistically significant gene groups. Aimed at reducing computational demand and streamlining feature selection, the model also addresses data redundancy by eliminating duplicate gene-disease associations. Application to nine human gene expression datasets from the GEO database showed promising results. It demonstrated improvements in computational efficiency and analytical precision while reducing the number of features selected per dataset compared to the traditional G-S-M approach, without compromising accuracy. These initial findings highlight the Pre-Scoring G-S-M model's potential to enhance transcriptomic data analysis, indicating a promising direction for future bioinformatics research. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Data accuracy

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PAGE-QUERIES AS A TOOL FOR ORGANIZING SECONDARY MEMORY AUXILIARY databaseS .2. OPTIMAL SELECTION OF SADB CONTENTS

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information sciences 1993年第1-2期69卷 127-156页

作者： KAMEL, N Computer and Information Sciences Department Database Systems Research and Development Center University of Florida Gainesville Florida 32611 USA

In this paper, a specific aspect of a new approach to organizing secondary memory redundancy in centralized databases is investigated. The approach is based on optimal management of a special type of redundant temporaries, collectively called the auxiliary database. The specific aspect investigated is the problem of optimal selection of the contents of the auxilary database. The new approach is based on exploiting the irregularities in both the distribution of data values in the database and in its access patterns. In order to exploit the nonuniformity in the distribution of both queries and data, special atomic data constructs, called page-queries are used as the smallest indivisible information units in the auxiliary database. A page-query is roughly defined as the result of one query when it is processed against only one memory page. It is shown that use of these atomic data packets as the only building blocks in secondary memory auxilary databases allows us to realize optimization potential that could otherwise not be achieved. The problem of maintaining the auxiliary database in response to database evolution is approached in a self-adaptive fashion by periodically reorganizing its contents to suit the common usage and data distribution patterns. A mathematical programming formulation of the problem is given for which a fast solution algorithm is known. The approach taken to handle updates is to keep the main database always up to date and to adaptively select the contents of the temporaries based on their update history. Measurements obtained from a real implementation are presented and provide preliminary verification of the proposed approach.

关键词： Query languages

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Hardware-Accelerated Detection of Unauthorized Mining Activities Using YOLOv11 and FPGA

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 967-979页

作者： Ghodhbani, Refka Saidani, Taoufik Kachoukh, Amani Elsayed, Mahmoud Salaheldin Said, Yahia Ahmed, Rabie Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Information Systems-Faculty of Computing and Information Technology Northern Border University Saudi Arabia Department of Computer Sciences-Faculty of Computing and Information Technology Northern Border University Saudi Arabia DCenter for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Computer Science-Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Mathematics and Computer Science Department-Faculty of Science Beni-Suef University Beni-Suef Egypt

Illegal mining activities present significant environmental, economic, and safety challenges, particularly in remote and under-monitored regions. Traditional surveillance methods are often inefficient, labor-intensive, and unable to provide real-time insights. To address this issue, this study proposes a computer vision-based solution leveraging the state-of-the-art YOLOv11 Nano and Small models, fine-tuned for the detection of illegal mining activities. A specific dataset comprising aerial and ground-level images of mining sites was curated and annotated to train the models for identifying unauthorized excavation, equipment usage, and human presence in restricted zones. The proposed system integrates the hardware-software design of YOLOv11 on the PynqZ1 FPGA, offering a highperformance, low-latency, and energy-efficient solution suitable for real-time monitoring in resource-constrained environments. This hardware-accelerated approach combines FPGA's parallel processing capabilities with the lightweight deep learning models, enabling efficient deployment for automated illegal mining detection. By providing a scalable, real-time monitoring tool, this work contributes to the development of automated enforcement tools for the mining industry, ensuring better control and surveillance of mining activities. To validate the efficiency of deep learning deployment on edge devices, YOLOv11n was implemented on an FPGA, utilizing 70% of available LUTs, 50% of FFs, and 80% of DSPs, with 8.3 Mbits of on-chip memory. The design achieved 100.33 GOP/s throughput, 18 FPS at 55 ms latency, consuming 4.8 W, and delivering an energy efficiency of 20.90 GOP/s/W. © (2025), (Science and information Organization). All rights reserved.

关键词： Mining industry

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Building Privacy-Preserving Medical Text Models With a Pretrained Transformer

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IEEE Internet of Things Journal 2025年第9期12卷 11529-11538页

作者： Shafiq, Muhammad Ren, Lijing Srivastava, Gautam Zhang, Denghui Bourouis, Sami Gadekallu, Thippa Reddy Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou511370 China Institute of Artificial Intelligence Guangdong Mechanical & Electrical Polytechnic Guangzhou510515 China Department of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Division of Research and Development Lovely Professional University Phagwara144411 India Center of Research Impact and Outcome Chitkara University Rajpura140401 India

The rapid advancement of big data and artificial intelligence (AI) in healthcare heightens the urgency for accurate medical text sentiment analysis. The privacy protection of medical data has been a crucial concern due to its sensitivity. The Internet of Medical Things (IoMT) facilitates large-scale data collection at lower cost, enabling precision medicine. However, decentralized IoMT poses novel challenges to centralized standard encryption schemes. In this article, we propose a novel approach to building privacy-preserving sentiment models with a generative pretrained transformer (GPT). We first convert sensitive medical text data into noise-like and distributed one-hot images. Then, we introduce visual cryptography (VC) for lightweight and secure transmission of medical text across public networks in resource-limited IoMT devices. We adopt a cross-domain sentiment analysis framework that finetunes transformer-based language models for accurate sentiment analysis instead of training GPT in sentiment analysis from scratch. Experimental results show that the proposed approach improves the accuracy and effectiveness of sentiment analysis while maintaining privacy, thereby addressing a significant gap in biomedical text analysis. © 2014 IEEE.

关键词： Differential privacy

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TEMPORAL databaseS WITH 2-DIMENSIONAL TIME - MODELING AND IMPLEMENTATION OF MULTIHISTORY

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information sciences 1994年第1-2期80卷 43-89页

作者： KIM, SK CHAKRAVARTHY, S Database Systems Research and Development Center Department of Computer and Information Sciences University of Florida Gainesville Florida 32611 USA

In this paper, we present an interpretation of the temporal database with two-dimensional time (also termed bitemporal databases in the literature) and propose an implementation. We define the notions of two-dimensional temporal validity and valid pattern. Using valid patterns, the notion of multihistory, which is a collection of a data object's history at every viewpoint, is developed. We show how a multihistory can be modeled using the relations. We define data manipulation operations over multihistory. Algorithms for implementing a multihistory are couched in the relational data model using the temporal data manipulation operations. Finally, set operations over multihistory are illustrated.

关键词： TEMPORAL databases

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RESOLUTION OF TIME CONCEPTS IN TEMPORAL databaseS

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information sciences 1994年第1-2期80卷 91-125页

作者： CHAKRAVARTHY, S KIM, SK Database Systems Research and Development Center Department of Computer and Information Sciences University of Florida Gainesville Florida 32611 USA

In this paper we identify and present three distinct time concepts for precise and lossless information preservation in temporal databases. We provide a formal definition of temporal validity and further extend it to the notion of interpretation-based validity, with which the confusion among various time concepts introduced earlier for temporal databases is resolved. Then, we discuss the problem of preserving multiple past states of a temporal database, which leads to the identification of a maximal set of time concepts. It is shown that the time concept event time is needed to correctly model retroactive and proactive updates, as it is not possible to model them using only the valid and transaction times as thought earlier. We also show the adequacy of three time concepts (event time, along with valid and transaction times) for completely preserving different past states generated by retroactive and proactive updates, error corrections, and delayed updates. In addition, we define the evolution of an object (i.e., object's history) along a single time dimension (valid time) by using temporal and interpretation-based validity. Finally, we sketch the implementation of history for the relational data model.

关键词： TEMPORAL databases RELATIONAL databases

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