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A Social Distancing Framework Based on GPS and Bluetooth Empowered by Feature-based Machine Learning Algorithm

IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 118-127页

作者： Khan, Muhammad Adnan Rehman, Abdur Abbas, Sagheer Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community’s insecurity is growing as a result of a lack of appropriate remedial measures and immunization against the disease. In this case, social distancing is thought to be an effective barrier against the spread of the contagion virus as the risk of virus transmission can be reduced by avoiding direct contact with people. Thus, the goal of this research is to develop and improve an AI (Artificial Intelligence) system architecture for social distance monitoring. The framework could also use the GPS (Global Positioning System) to recognize human separation through cell phones. The transition learning framework is also applied to improve the consistency of the existing system. In this manner, the detection system uses a pre-trained technique that takes a Bluetooth dataset and location-sharing dataset to link to an additional level. In an attempt to approximate social distancing breaches among people, we used Bluetooth technology along with GPS distance estimation and set a threshold. To predict if the distance value exceeds the required social distance standard, a violation threshold is calculated and then it sends an alarm to every individual who is not maintaining social distancing. In response, the individual who breaks the social distance limit is also monitored using a detection approach. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Improved channel estimation for underwater wireless optical communication OFDM systems by combining deep learning and machine learning models

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Optical and Quantum Electronics 2025年第3期57卷 1-21页

作者： Salama, Wessam M. Aly, Moustafa H. Department of Computer Engineering Faculty of Engineering Pharos University Canal El Mahmoudia Street Beside Green Plaza Complex 21648 Alexandria Egypt OSA Member Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Marine Transport Alexandria1029 Egypt

Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to improve the channel estimation and lower the error rate. In order to mitigate inter-symbol interference and map the datasets, this paper introduces M-quadrature amplitude modulation (16-QAM) and orthogonal frequency division multiplexing (OFDM), which is based on quadrature phase shift keying (QPSK). Additionally, the existence or lack of cyclic prefixes forms the basis of our simulation. Additionally, the suggested models are investigated using pilot samples 2, 4, 8, and 64. Labeled OFDM signal samples, where the labels match the signal received after applying OFDM and passing through the medium, are used to train the proposed models. The DenseNet121 functions as a powerful feature extractor to extract intricate spatial information from received signal data. Sequential models like as RNN, LSTM, and GRU are used to model temporal dependencies in the retrieved features. RF is also utilized to exploit non-linear relationships and interactions between features to further increase prediction accuracy and reduce bit error rate (BER). By comparing the models using key metrics like accuracy, bit error rate (BER), and mean squared error (MSE), superior performance is attained based on the DenseNet121_RNN_GRU_RF model. Additionally, the DLMs are assessed against traditional methods like minimal mean square error (MMSE) and least squares (LS). Using the DenseNet121_RNN_GRU_RF model indicates a considerable gain over alternative architectures, with an improvement of 36.3% over DensNet121-RNN-LSTM-RF, according to a comparison of the suggested models without cyclic prefix for OFDM_QPSK. The improvement in percentages of roughly 63.3% over DensNet121-RNN-LSTM, 68.18% over De

关键词： Orthogonal frequency division multiplexing

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Algorithms for the Collaborative Delivery Problem with Monitored Constraints 19th

Algorithms for the Collaborative Delivery Problem with Mo...

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19th International Conference and Workshops on Algorithms and Computation, WALCOM 2025

作者： Blank, Lotte Huynh, Kien C. Luo, Kelin Naredla, Anurag Murty Institute of Computer Science University of Bonn Bonn Germany Communications and Transport Systems ITN Linköping University Linköping Sweden Department of Computer Science and Engineering University at Buffalo GetzvilleNY United States

ISBN: (纸本)9789819628445

We provide efficient algorithms to solve package delivery problems in which a sequence of drones work together to ‘optimally’ deliver a package from a source s to a target t. The package may be transferred from one drone to another only on a given line on which secure package transfers are ensured (we refer to this as monitored constraints). We allow the source and target to lie outside of this line. The drones have different starting locations, speeds, and rates of fuel consumption. Two notions of optimality are studied—fuel-efficient delivery (minimize total fuel consumption in a successful delivery), and fastest delivery (minimize total time for delivery). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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Cloud Computing and Neutrosophic Logic: Navigating Uncertainty in Educational Administration

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Neutrosophic Sets and Systems 2025年 78卷 102-449页

作者： Yasser, Ibrahim Abd El-Khalek, Aya A. Khalid, Huda E. Salama, A.A. Essa, Ahmed K. Engineering Faculty University of Mansoura Mansoura35516 Egypt Communications and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura35524 Egypt Telafer University The Administration Assistant for the President of the Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Sciences Port Said University Port Said42526 Egypt

Educational institutions face inherent uncertainties in student performance, stakeholder priorities, and data analysis. This paper explores how cloud computing, with its data storage, analytics, and collaboration tools, can address these uncertainties in educational administration. We propose a neutrosophic framework utilizing "truth," "falsity," and "indeterminacy" values to represent these complexities within educational data. Real-world case studies analyze how cloud computing can reduce uncertainties and improve decision-making. Our findings reveal that cloud computing offers significant potential for navigating the "fog" of educational administration, but its effectiveness is enhanced by considering the "indeterminacy" inherent in educational data through the neutrosophic framework. This research contributes a unique neutrosophic perspective to the use of cloud computing for improved and more informed decision-making in educational administration. © 2025, University of New Mexico. All rights reserved.

关键词： Case Studies Cloud Computing Decision-Making Educational Administration Educational Data Neutrosophic Logic Uncertainty

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Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting

Bi-Directional MS Lesion Filling and Synthesis Using Denoisi...

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Medical Imaging 2025: Image Processing

作者： Zhang, Jinwei Zuo, Lianrui Liu, Yihao Remedios, Samuel Landman, Bennett A. Prince, Jerry L. Carass, Aaron Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781510685901

Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brain parcellation. However, the presence of lesion often complicates these analyses, particularly in brain parcellation. Lesion filling is commonly used to mitigate this issue, but existing lesion filling algorithms often fall short in accurately reconstructing realistic lesion-free images, which are vital for consistent downstream analysis. Additionally, the performance of lesion segmentation algorithms is often limited by insufficient data with lesion delineations as the training labels. In this paper, we propose a novel approach leveraging Denoising Diffusion Implicit Models (DDIMs) for both MS lesion filling and synthesis based on image inpainting. Our modified DDIM architecture, once trained, enables both MS lesion filing and synthesis of lesioned tissues. Specifically, it can generate lesion-free T1-weighted or FLAIR images from those containing lesions;Or it can add lesions to T1-weighted or FLAIR images of healthy subjects. The former is essential for downstream analyses that require lesion-free images, while the latter is valuable for augmenting training datasets for lesion segmentation tasks. We validate our approach through initial experiments in this paper and demonstrate promising results in both lesion filling and synthesis, paving the way for future work. © 2025 SPIE.

关键词： Image segmentation

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Cumulative histogram as a feature selection technique for anomaly detection

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Multimedia Tools and Applications 2025年第12期84卷 10095-10107页

作者： Nassar, Mostafa Salama, Rania A. Saleeb, Adel A. El-bahnasawy, Nirmeen A. Ahmed, Hossam Eldin H. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology Faculty of Computers and Information Suez University Suez Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, Information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques. © The Author(s) 2024.

关键词： Anomaly detection

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COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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Cybersecurity advancements for medical image transmission: a hybrid optical-based cryptosystem harnessing chaos, DNA sequences, and mandelbrot keys

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Multimedia Tools and Applications 2025年 1-41页

作者： Alalwan, Nasser El-Shafai, Walid Amoon, Mohammed Benjdira, Bilel Department of Computer and Communication Engineering King Saud University Riyadh Saudi Arabia Prince Sultan University Riyadh11586 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

In today's advanced technological age, characterized by innovations like big data processing, cloud computing, and the Internet of Things (IoT), there is a rising utilization of medical multimedia data, especially medical images. These images, integral to the Internet of Healthcare Things (IoHT), necessitate secure transmission due to the increasing risks of unauthorized breaches and tampering. Current security methods, especially for cloud and mobile platforms, often struggle with challenges related to processing capacity, memory use, data size, and energy, making them ill-suited for extensive medical data or resource-limited environments. To address these challenges, this study introduces a novel hybrid cryptosystem, drawing on the unique qualities of the optical Arnold chaotic map, DNA (DeoxyriboNucleic Acid) sequences, and Mandelbrot keys, providing a fortified approach to the secure streaming of medical images. The proposed framework operates via a precise and structured procedure. It begins by applying the optical Arnold chaotic map cipher to each of the three-color channels (R, G, and B) within a medical image. This is followed by overlaying DNA encoding sequences on the resultant encrypted image from the earlier ciphering phase. Leveraging this groundwork, we incorporate an advanced Mandelbrot set-driven shift mechanism specifically designed to create complex confusion patterns within the R, G, and B segments of the encrypted medical imagery. The efficacy of the proposed cryptosystem is rigorously substantiated through an extensive array of simulations supported by a comprehensive security analysis. The results highlight its unparalleled resilience and security capabilities in the realm of medical image encryption, marking a significant leap over previous systems in the literature. Essentially, our work pioneers a solution to a pressing challenge in medical image security, ensuring enhanced protection of delicate health data among the rapidly evolving advanc

关键词： Cybersecurity

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