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Digital-Twin-Enhanced Deep Reinforcement Learning for Intelligent Omni-Surface Configurations in MU-MIMO Systems

IEEE Internet of Things Journal 2025年第9期12卷 13005-13020页

作者： Ye, Xiaowen Yu, Xianghao Fu, Liqun Xiamen University School of Informatics Department of Communication Engineering Xiamen361005 China City University of Hong Kong Department of Electrical Engineering Hong Kong

Intelligent omni-surface (IOS) is a promising technique to enhance the capacity of wireless networks, by reflecting and refracting the incident signal simultaneously. Traditional IOS configuration schemes, relying on all subchannels’ channel state information and user equipments’ mobility, are difficult to implement in complex realistic systems. Existing works attempt to address this issue employing deep reinforcement learning (DRL), but this method requires a lot of trial-and-error interactions with the external environment for efficient results and thus cannot satisfy the real-time decision making. To enable model-free and real-time IOS control, this article puts forth a new framework that integrates DRL and digital twins. As a first step, deep reinforcement learning IOS (DeepIOS), a DRL based IOS configuration scheme with the goal of maximizing the sum data rate, is developed to jointly optimize the phase-shift and amplitude of IOS in multiuser multiple-input-multiple-output (MU-MIMO) systems. Thereafter, in order to further reduce the computational complexity, DeepIOS introduces an action branch architecture, which decides two optimization variables in parallel in a separate fashion. Finally, a digital twin module is constructed through supervised learning as a preverification platform for DeepIOS, such that the decision making’s real-time can be guaranteed. The formulated framework is a closed-loop system, in which the physical space provides data to establish and calibrate the digital space, while the digital space generates a large number of experience samples for DeepIOS training and sends the trained parameters to the IOS controller for configurations. Numerical results show that compared with random and MAB schemes, the proposed framework attains a higher data rate and is more robust to different settings. Furthermore, the action branch architecture reduces DeepIOS’s computational complexity, and the digital twin module improves DeepIOS’s convergence speed

关键词： Deep reinforcement learning

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Enhancing the Traditional Batik Design Practices: An Approach to Batik Motif Design Using Artificial Intelligence

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第2期52卷 317-324页

作者： Ardhianto, Peter Santosa, Yonathan Purbo Anandhita, Gustav Visual Communication Design Department Soegijapranata Catholic University Semarang59234 Indonesia Informatics Engineering Department Soegijapranata Catholic University Semarang59234 Indonesia Architecture Department Soegijapranata Catholic University Semarang59234 Indonesia

The development of hand-drawn batik motifs has decreased significantly as well and many motifs have not been documented. This research presents artificial intelligence generative approach to increase variance in the domain of Bakaran batik motifs. This research introduces three different versions of the Stable Diffusion algorithm: Version 1.5, Version 2.0, and SDXL. This study explores the capabilities of Stable Diffusion in generating Bakaran batik motifs. The study concludes that Version 1.5 and SDXL are the most effective models, each with unique strengths and trade-offs. SDXL is ideal for artists seeking to push the boundaries of traditional batik design, while Version 1.5 preserves traditional patterns and ensures cultural authenticity. Evaluation using VGG-11 supports SDXL’s superior performance in generating batik patterns, whereas Version 2.0 consistently underperforms across all metrics. This research demonstrates the versatility of the Stable Diffusion with LoRA technique when applied to batik motif. By combining deep learning methods with batik motifs, our findings open up new opportunities in the convergence of artistic creativity and technological innovation. This study may help to increase the productivity of batik industry players, especially workflow trimming at the ideation stage. © (2025), (International Association of Engineers). All rights reserved.

关键词： Deep learning

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Joint Optimization of Reconfigurable Intelligent Surface Phase Shifts and Power Allocation in LEO-NOMA Systems Using Soft Actor-Critic

Joint Optimization of Reconfigurable Intelligent Surface Pha...

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2025 Systems of Signals Generating and Processing in the Field of on Board communications, SOSG 2025

作者： Yahia, Khalil M. Wahbi, Ibrahim S. Klimov, Dmitry A. Dawood, Tammam Z. Moscow Technical University of Communications and Informatics Moscow Russia Damascus University Department of Electronics and Communication Engineering Damascus Syria

ISBN: (纸本)9798331532635

Combining Reconfigurable Intelligent Surfaces (RIS) with Non-Orthogonal Multiple Access (NOMA) offers a promising way to improve spectral efficiency and energy utilization in Low Earth Orbit (LEO) satellite networks. However, the highly dynamic nature of LEO environments make it challenging to optimize RIS phase shifts and power allocation in real time. This paper introduces a reinforcement learning-based framework that applies the Soft Actor-Critic (SAC) algorithm to jointly optimize RIS configurations and power allocation among users. The proposed method works in two stages: first, SAC fine-tunes RIS phase shifts while keeping power allocation fixed;then, it dynamically adjusts power levels to enhance overall system performance. This method, in contrast to traditional strategies that depend on perfect channel state information (CSI), focuses on adaptive decision-making aimed at maximizing rewards, effectively mitigating the impact of channel estimation errors during training. Simulation results confirm that the SAC-based framework effectively boosts channel capacity without requiring additional transmission power. Furthermore, integrating RIS with NOMA further enhances performance, making it a strong candidate for next-generation satellite communications. © 2025 IEEE.

关键词： Energy utilization

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Keio University Department of Information and Computer Science Kanagawa223852 Japan Tohoku University International Research Institute of Disaster Science Sendai Japan

As Synthetic Aperture Radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this paper proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network (AMCW-NN). Firstly, employing Multi-Scale Superpixel Reconstructed Difference Image (MSRDI) effectively enhances the edge information of the images. Secondly, a twostage Weighted Center Constrained Fuzzy C-Means (WCCFCM) clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Thirdly, a Convolutional Squeeze-and-Excitation (SE) Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model (DDPM) is introduced into the CSWNN to expand pseudo-labeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2%-9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Edge AI-powered marine pollution classification with customized CNN model

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Neural Computing and Applications 2025年第9期37卷 6449-6463页

作者： Palanisamy, Sanjai Bonny, Talal Nasir, Nida Al Shabi, Mohammad Al Shammaa, Ahmed University of Sharjah Sharjah United Arab Emirates Khorfakkan University Sharjah United Arab Emirates College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Mechanical Department College of Engineering University of Sharjah Sharjah United Arab Emirates Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

The increasing production of disposable plastic products contributes greatly to marine pollution and its impact on the marine ecosystem and organisms consuming ocean-derived food. To address this issue, this paper proposes a new customized convolutional neural network (CNN) model for categorizing the level of marine pollution in underwater ocean regions using image classification. The customized CNN model is developed and compared with five preexisting models, including DenseNet121, Inception-ResNetV2, InceptionV3, VGG-19, and VGG-16. The results show that the customized model achieves an accuracy of 99.5% and performs optimally according to various performance metrics. The model is implemented on an edge AI device, such as Raspberry Pi, to bring it to practical use. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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BCDM: A Novel AI-Based Model for Detection of Breast Cancer 1st

BCDM: A Novel AI-Based Model for Detection of Breast Cancer

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1st International Conference on Intelligent Systems, Blockchain, and communication Technologies, ISBCom 2024

作者： Shaban, Warda M. Moustafa, Hossam El-Din El-Seddek, Mervat M. Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and Informatics Horus University New Damietta Egypt Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Electronics and Communications Engineering Faculty of Engineering Horus University New Damietta Egypt

ISBN: (纸本)9783031823763

Breast cancer is a prevalent and significant form of cancer that has a profound impact on women worldwide, and it is the leading cause of mortality. Timely identification of breast cancer is a crucial procedure that can enable suitable therapy, hinder the advancement of cancerous cells, and decrease rates of illness and death. Machine Learning (ML) is highly favored techniques for precise detection and classification of breast cancer. This paper introduces a unique method for categorizing breast cancer by utilizing a novel technique for selecting relevant attributes and employing ML algorithms. This method is referred to as the Breast Cancer Detection Model (BCDM). The BCDM system comprises three distinct phases, namely data preprocessing, feature selection, and patient detection. The primary objective of the data preprocessing phase is to extract distinctive characteristics from mammogram images and eliminate any anomalous items. This is accomplished by employing an innovative feature selection technique known as Enhanced Bat Algorithm (EBA) to identify the most influential and meaningful attributes from the extracted ones. Afterwards, these attributes are entered into the patient detection phase to determine whether the patient is unaffected or has either benign or malignant breast cancer. Based on the obtained results, the proposed method surpasses rivals in performance in terms of accuracy, precision, recall, and F-score. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Autonomous vehicle surveillance through fuzzy C-means segmentation and DeepSORT on aerial images

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PeerJ Computer Science 2025年 11卷 1-23页

作者： Qureshi, Asifa Mehmood Alotaibi, Moneerah Alotaibi, Sultan Refa AlHammadi, Dina Abdulaziz Jamal, Muhammad Asif Jalal, Ahmad Lee, Bumshik Department of Computer Science Air University Islamabad Pakistan Department of Computer Science College of Science and Humanities Shaqra University Dawadmi Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Information and Communication Engineering Chosun University Gwangju Korea Republic of Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of

The high mobility of uncrewed aerial vehicles (UAVs) has led to their usage in various computer vision applications, notably in intelligent traffic surveillance, where it enhances productivity and simplifies the process. Yet, there are still several challenges that must be resolved to automate these systems. One significant challenge is the accurate extraction of vehicle foregrounds in complex traffic scenarios. As a result, this article proposes a novel vehicle detection and tracking system for autonomous vehicle surveillance, which employs Fuzzy C-mean clustering to segment the aerial images. After segmentation, we employed the YOLOv4 deep learning algorithm, which is efficient in detecting small-sized objects in vehicle detection. Furthermore, an ID assignment and recovery algorithm based on Speed-Up Robust Feature (SURF) is used for multi-vehicle tracking across image frames. Vehicles are determined by counting in each image to estimate the traffic density at different time intervals. Finally, these vehicles were tracked using DeepSORT, which combines the Kalman filter with deep learning to produce accurate results. Furthermore, to understand the traffic flow direction, the path trajectories of each tracked vehicle is projected. Our proposed model demonstrates a noteworthy vehicle detection and tracking rate during experimental validation, attaining precision scores of 0.82 and 0.80 over UAVDT and KIT-AIS datasets for vehicle detection. For vehicle tracking, the precision is 0.87 over the UAVDT dataset and 0.83 for the KIT-AIS dataset. © 2025 Qureshi et al.

关键词： Image segmentation

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Precision Multiband Terahertz Metamaterial Biosensor With Targeted Spectral Selectivity for Early Detection of MCF-7 Breast Cancer Cells

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IEEE Sensors Journal 2025年第9期25卷 14970-14987页

作者： Hamza, Musa N. Alibakhshikenari, Mohammad Virdee, Bal S. Lavadiya, Sunil Ud Din, Iftikhar Cavalcante De Souza Sanches, Bruno Koziel, Slawomir Iffat Naqvi, Syeda Panda, Abinash Farmani, Ali Rashid, Nasr Kouhalvandi, Lida Shariatifar, Milad University of Raparin College of Science Department of Physics Sulaymaniyah46012 Iraq University of Rome Tor Vergata Department of Electronics Engineering Rome00133 Italy London Metropolitan University Department of Center for Communications Technology LondonN7 8DB United Kingdom Marwadi University Department of Information and Communication Technology Gujarat Rajkot360003 India University of Engineering and Technology Department of Telecommunication Engineering Mardan23200 Pakistan Escola Politécnica da Universidade de São Paulo Department of Electronic Systems Engineering São Paulo05508 Brazil Reykjavik University Engineering Optimization and Modeling Center Reykjavik102 Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland University of Engineering and Technology Department of Telecommunication Engineering Taxila34109 Pakistan CMR Institute of Technology Department of Electronics and Communication Engineering Bengaluru560037 India Lorestan University Department of Electronics engineering Khorramabad68151-44316 Iran Jouf University College of Engineering Department of Electrical Engineering Sakaka72388 Saudi Arabia Al-Azhar University Faculty of Engineering Department of Electrical Engineering Cairo11884 Egypt Dogus University Department of Electrical and Electronics Engineering Istanbul34775 Turkey

Cancer remains one of the leading causes of mortality worldwide, with breast cancer (BC) being a particularly prevalent form. Projections estimate nearly 20 million new cases globally over the next two decades. Early detection is critical for effective treatment;however, conventional diagnostic techniques often lack the necessary sensitivity and specificity, with some methods being invasive and labor-intensive. Recent advancements in microwave imaging (MWI) have shown significant potential as efficient, noninvasive tools for monitoring various cancer types. MWI operating in the terahertz (THz) range has emerged as a promising approach for biosensing, offering the precision needed to differentiate between healthy and cancerous tissues by analyzing small-scale biological features. Among the methods for BC detection, the identification and analysis of MCF-7 BC cells are particularly significant. THz waves interact uniquely with the intrinsic properties of MCF-7 cells, making THz-based biosensors ideal candidates for diagnostic tools. However, many existing sensors are limited in key performance areas, including operating bandwidth and absorption efficiency. This study introduces a novel multiband metamaterial (MTM)-based biosensor specifically designed for the detection of MCF-7 BC cells. The sensor features a compact geometry composed of multiple resonators made from 200-nm-thick aluminum (Al) layers on a 50-$\mu $ m-thick polyethylene terephthalate (PET) substrate. With dimensions of only $198\times 198μ $ m2, the proposed device is exceptionally compact. It operates in the 0.5-1.6-THz frequency range and achieves near-perfect absorption rates (>99%) across multiple bandwidths. These results are achieved through precise tuning of the sensor's geometry and architectural optimization, significantly enhancing its sensitivity for cancer detection. Comprehensive validation of the sensor is performed using full-wave electromagnetic analysis, which includes evaluating elect

关键词： Terahertz waves

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