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Optimizing Secure Multi-User ISAC systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Identify Assessment Indicators in Online Learning to Increase Student Performance Using Neural Network Approach 9

Identify Assessment Indicators in Online Learning to Increas...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Rabiha, Sucianna Ghadati Dewi, Meta Amalya Murad, Dina Fitria Kurniadi, Felix Indra Romli, Awanis Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universiti Malaysia Pahang Faculty of Computing Pahang Malaysia

ISBN: (纸本)9798350383010

The main point of predicting student performance is to identify students at-risk as early as possible to enable instructors to implement educational interventions and facilitate student achievement. In this study, we have used Neural Network aproach to identify assement indicator in Online Learning. The dataset used is Distance Learning Student Value Data extracted through Learning Management systems The highest accuracy value obtained was 99.61% with a precision value of 99.37%, a recall of 98.87% and a relatively low RMSE value of 5.5%. The best accuracy results from the training data distribution ratio of 70% and 30%, while the proposed number of hidden layers is 2 hidden layers with the number of neurons in each layer H1(Hidden layer 1) and H2(Hidden layer 2) is 12. And the value of learning rate is 0.04. This study contributes to the selection of Neural Network architectures in determining student performance predictions. In addition, it has an impact on the analysis of which variables affect student passing grades. So that it can be a reference in determining content improvements and further learning models in Online Learning. © 2024 IEEE.

关键词： Contrastive Learning

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer systems science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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End-to-end Multilingual Coreference Resolution with Headword Mention Representation 7

End-to-end Multilingual Coreference Resolution with Headword...

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7th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2024

作者： Pražák, Ondřej Konopík, Miloslav Department of Computer Science and Engineering NTIS - New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň306 14 Czech Republic

ISBN: (纸本)9798891761711

This paper describes our approach to the CRAC 2024 Shared Task on Multilingual Coreference Resolution. Our model is based on an end-to-end coreference resolution system. Apart from joined multilingual training, we improved our results with headword mention representation and training large model mT5-xxl through LORA. We provide an analysis of the performance of our model. Our system ended up in 4th place. Moreover, we reached the best performance on three datasets out of 21. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Report on the 11th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2023) 5

Report on the 11th International Workshop on Quantitative Ap...

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Joint of the 5th International Workshop on Experience with SQuaRE Series and its Future Direction and the 11th International Workshop on Quantitative Approaches to Software Quality, IWESQ-QuASoQ 2023

作者： Lichter, Horst Sunetnanta, Thanwadee Anwar, Toni Research Group Software Construction RWTH Aachen University Aachen Germany Computer Science Academic Group Faculty of Information And Communication Technology Mahidol University Bangkok Thailand Faculty of Science and Information Technology Computer & Information Systems Universiti Teknologi Petronas Bandar Seri Iskandar Perak Malaysia

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Intelligent Models for Forecasting Repair Timing of Leakage Water Pipelines 3

Intelligent Models for Forecasting Repair Timing of Leakage ...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Mohamed Warad, Ahmed Ali Wassif, Khaled Darwish, Nagy Ramadan Faculty of Graduate Studies for Statistical Research Department of Information Systems and Technology Cairo Egypt Faculty of Computers and Artificial Intelligence Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

Machine Learning (ML) is a branch of artificial intelligent field, which has great capability to achieve improvements in real industries. On the other side, water leakage is a major problem for water utilities;it can waste water and damage infrastructure, during long repair times. There is no previous research using ML algorithms to forecast the repair time of water pipe leakage. This paper investigates seven ML algorithms for repair time forecasting in water pipe leakage. The paper uses data from a water utility in Alexandria, Egypt. Results exposed that all seven models were able to forecast the time of repair;in particular, AdaBoost, is the best, has an RMSE of 0.057. This study highlights the ML approaches for predicting the time needed to repair a water pipe leakage and the need for further research to improve the comprehension of the relation between repair time and pipe features. © 2023 IEEE.

关键词： Adaptive boosting

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Applying C atboost Regression Model for Prediction of House Prices

Applying C atboost Regression Model for Prediction of House ...

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2022 International Conference on Engineering and MIS, ICEMIS 2022

作者： Almejrb, Rafea. M. Sallabi, Omar M. Mohamed, Abdelhafid Ali College of Computer Technology Department of Computer Science Benghazi Libya Faculty of Information Technology Department of Information Systems Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Every year, the cost of houses rises, demanding the development of a method to forecast home prices in the future. This paper aims to apply the Catboost regression model by changing the iteration and learning rate. A feature and house value dataset for King County, Washington, is used. During the pre-processing of the data, extreme values are winsorized, and features with a high degree of correlation are removed. There are twelve possible models, including Catboost, RandomForestRegressor, and KNeighborsRegressor. Several metrics are used to assess them, such as Mean Squared Error (MSE), R-squared score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) (MSE). The model with a low RMSE of 0.013166 performs well compared to other research, especially in the test set where R2 is 0.915256. In this study, Catboost is the model that performs the best overall and can be used to estimate home prices. The most significant factors affecting property prices are location, living area, and house condition. It is confirmed that the conclusions in this research are consistent with real-world experience after comparing and contrasting with other works. © 2022 IEEE.

关键词： Houses

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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