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Elevating E-Service Standards in Bangladesh: Merging FAHP and TOPSIS for Strategic Online Business Solutions

IEEE Access 2025年 13卷 83765-83780页

作者： Farzana Tasnim Tanjim Mahmud Shefayatuj Johara Chowdhury Tasnia Fahrin Choity Aisha Ahmed Mohammad Saeed Hasan Chowdhury Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Electrical and Electronic Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Skellefteå Sweden

The rapid expansion of Bangladesh’s digital economy has unlocked new opportunities for online businesses, but it has also heightened the challenges of delivering high-quality e-services in an increasingly competitive environment. While previous studies have largely focused on customer satisfaction and consumer experiences, the perspective of business owners—those managing daily operations and strategic decisions—has been notably underexplored. Addressing this gap, the present study aims to identify and prioritize the critical success factors (CSFs) influencing the performance of online businesses in Bangladesh from the owners’ viewpoint.A structured survey has been conducted among 57 online business owners, collecting data across ten essential dimensions, including website infrastructure, information sharing, delivery practices, responsiveness, customization, customer service, reliability, payment security, return policies, and service quality challenges. To ensure a data-driven analysis, the Random Forest algorithm is utilized for feature importance extraction, followed by pairwise comparisons through fuzzy triangular matrices. Subsequently, FAHP and TOPSIS techniques are applied to systematically rank the factors and alternatives. Model validation using the XGBoost classifier results in 93% accuracy and a 93% F1-score, demonstrating the robustness of the proposed approach. The results reveal that service quality concerns, payment security, and effective customer support are the most influential determinants of business success. Beyond offering a methodological advancement that integrates machine learning with MCDM, this study delivers actionable insights for business owners, digital strategists, and policymakers aiming to enhance the competitiveness and resilience of Bangladesh’s e-commerce landscape.

关键词： Business Random forests Decision making Accuracy Feature extraction Electronic commerce Surveys Security Reliability Q-factor

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AAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

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IEEE Internet of Things Journal 2025年第8期12卷 10834-10850页

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C. M. Jilin University College of Computer Science and Technology Changchun 130012 China Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun 130012 China Nanyang Technological University College of Computing and Data Science Jurong West Singapore Jilin University Department of Communication Engineering Changchun 130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen 518115 China Shenzhen University College of Computer Science and Software Engineering Shenzhen 518060 China The University of British Columbia Department of Electrical and Computer Engineering Vancouver V6T 1Z4 BC Canada

Autonomous aerial vehicles (AAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the Line of Sight (LoS) dominance in air-to-ground (A2G) communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This article explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-AAV network systems. Specifically, the AAV swarm forms an AAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a tradeoff tradeoff between the effectiveness of CB-based interference mitigation and the energy conservation of AAVs. Thus, by optimizing the excitation current weights and hover position of AAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multiobjective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic nondominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by AAVs to nonreceiving BSs. © 2014 IEEE.

关键词： autonomous aerial vehicle (AAV) network Collaborative beamforming (CB) evolutionary computation interference mitigation multiobjective optimization

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Orthogonal Time Frequency Space with Sub-Band Index Modulation

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IEEE Communications Letters 2025年第5期29卷 1047-1051页

作者： Zhang, Si-Yu Zhang, Yue-Xia Shahrrava, Behnam Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Key Laboratory of Information and Communication Systems Ministry of Information Industry School of Information and Communication Engineering Beijing100101 China University of Windsor Department of Electrical and Computer Engineerning Windsor N9B 3P4 Canada

This letter proposes an innovative orthogonal time-frequency space with sub-band index modulation (OTFS-SBIM) scheme. To overcome the complexity and spectral efficiency (SE) limitations inherent in the OTFS-IM, this study introduces a transmission mechanism where sub-carriers are equally divided into two sub-bands, and the index modulation (IM) operations are restricted to a single sub-band. To offset the potential SE loss, the proposed OTFS-SBIM employs a band-controlling bit β and performs IM independently in the in-phase and quadrature (I/Q) dimensions. These improvements ensure both the diversity gain and enhanced bit error rate (BER) performance. Further, a low-complex maximum likelihood (ML) detection is proposed for bit recovery. Finally, analytical expressions for average bit error probability (ABEP) and peak-to-average power ratio (PAPR) are derived and used to evaluate the OTFS-SBIM’s performance. Simulation results demonstrate that the proposed scheme outperforms the OTFS-IM scheme in terms of the BER and PAPR metrics while maintaining higher SE. © 1997-2012 IEEE.

关键词： Signal modulation

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Practical aberration correction using deep transfer learning with limited experimental data

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Optics Express 2025年第6期33卷 14431-14444页

作者： Kok, Yong En Bentley, Alexander Parkes, Andrew J. Somekh, Michael G. Wright, Amanda J. Pound, Michael P. University of Nottingham School of Computer Science NottinghamNG8 1BB United Kingdom Optics and Photonics Group Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Research Center for Humanoid Sensing Zhejiang Laboratory Hangzhou3111100 China

Adaptive optics is a technique for correcting aberrations and improving image quality. When adaptive optics was first used in microscopy, it was common to rely on iterative approaches to determine the aberrations present. It is advantageous to avoid iteration, and therefore there has been a shift to deep learning for aberration prediction. However, issues remain regarding the practicalities of machine learning for adaptive optics, an important one being the requirement for a large training dataset. Here, we explore transfer learning to overcome this need for data by pre-training a network on a large simulated dataset and fine-tuning it with reduced experimental data for application in an experimental setting. We demonstrate that the pre-trained network can make noticeable improvements with fine-tuning on just 24 experimental samples. To further enhance practicality, we significantly extend the range of aberrations present, predicting up to 25 Zernike modes with each coefficient ranging from −1 to 1, and perform a thorough analysis of the type and magnitude of phase-diversity required in the input data for a successful network. Our approach demonstrates substantial aberration reduction on experimental data for 10 Zernike modes, with an average 73% decrease in RMS wavefront error from 1.81 to 0.48 rad when correction is applied. This method achieves complete experimental image capture and aberration inference at rates comparable to the image acquisition time of a typical laser scanning microscope. Additionally, we consider the benefits of further improvements via an iterative step. As such, this work addresses some of the key practical hurdles that remain in the use of deep learning for aberration prediction and correction. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Adaptive optics

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InterQ: A DQN Framework for Optimal Intermittent Control

arXiv

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arXiv 2025年

作者： Aggarwal, Shubham Maity, Dipankar Başar, Tamer Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States

In this letter, we explore the communication-control co-design of discrete-time stochastic linear systems through reinforcement learning. Specifically, we examine a closed-loop system involving two sequential decision-makers: a scheduler and a controller. The scheduler continuously monitors the system’s state but transmits it to the controller intermittently to balance the communication cost and control performance. The controller, in turn, determines the control input based on the intermittently received information. Given the partially nested information structure, we show that the optimal control policy follows a certainty-equivalence form. Subsequently, we analyze the qualitative behavior of the scheduling policy. To develop the optimal scheduling policy, we propose InterQ, a deep reinforcement learning algorithm which uses a deep neural network to approximate the Q–function. Through extensive numerical evaluations, we analyze the scheduling landscape and further compare our approach against two baseline strategies: (a) a multi-period periodic scheduling policy, and (b) an event-triggered policy. The results demonstrate that our proposed method outperforms both baselines. The open source implementation can be found at https://***/AC-sh/InterQ. Copyright © 2025, The Authors. All rights reserved.

关键词： Discrete time control systems

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ARUC-GAN: A CycleGAN-Based Attention Residual U-Net for Low-Dose CT Denoising in Smart Healthcare

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IEEE Access 2025年 13卷 72544-72557页

作者： Mansour, Mohammad Djenouri, Djamel Said, Lobna A. Latif, Shahid Maalel, Ahmed Abdelhadi, Ameer M. S. McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4L8 Canada University of the West of England School of Computing and Creative Technologies BristolBS16 1QY United Kingdom Giza12588 Egypt University of Sousse ISSAT Sousse RIADI Laboratory National School of Computer Science ENSI Sousse4003 Tunisia

Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover, conventional denoising methods struggle with the non-uniform noise distributions in LDCT images and often require complex projection data, which limits their effectiveness and generalizability. Herein, we propose an AI-based denoising approach using an Attention Residual U-Net (ARU-Net) architecture integrated into the CycleGAN framework named Attention Residual U-Net CycleGAN (ARUC-GAN). The proposed framework outperforms state-of-the-art denoising models, such as RED-CNN, EDCNN, and CTformer, according to experimental evaluation on the Mayo Clinic abdominal CT dataset. The model exhibits a peak signal-to-noise ratio (PSNR) of 34.82 dB, a structural similarity index (SSIM) of 0.85, and an RMSE of 0.018. Furthermore, the model successfully maintains edge structures with an Edge Keeping Index (EKI) of 0.835. The visual examination validates ARUC-GAN’s superior texture and detail preservation. The results indicate that the suggested method has great promise as a smart health tool for improving diagnosis accuracy in LDCT scans. © 2013 IEEE.

关键词： Electronic health record

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Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey

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IEEE Internet of Things Journal 2025年第10期12卷 13384-13397页

作者： Zhang, Tao Kong, Fanyu Deng, Dongshang Tang, Xiangyun Wu, Xuangou Xu, Changqiao Zhu, Liehuang Liu, Jiqiang Ai, Bo Han, Zhu Deng, Robert H. Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui243002 China Minzu University of China School of Information Engineering Beijing100081 China Chongqing University College of Computer Science Chongqing400044 China Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Jiaotong University School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Singapore Management University Department of Information Systems Singapore Singapore

Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and Distributed Denial of Service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of Moving Target Defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this paper, we firstly provide a thorough analysis of the threats in the cloud-edge-terminal network. Then we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network. © 2014 IEEE.

关键词： Cloud security

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Lossy Compression: An Online Multi-Stage Technology for High-Fidelity Synchro-Waveform Measurements

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IEEE Transactions on Industry Applications 2025年

作者： Qiu, Wei Yin, He Wu, Yuru Dong, Yuqing Zheng, Yao Yao, Wenxuan Liu, Yilu Hunan University Department of Electrical Engineering and Computer Science Changsha410082 China University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States

Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. The outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements. © 1972-2012 IEEE.

关键词： Synchros

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MEMS-based twisted bilayer flat optics for multidimensional optical modulation and hyperimaging 14

MEMS-based twisted bilayer flat optics for multidimensional ...

引用

High Contrast Metastructures XIV 2025

作者： Tang, Haoning Lou, Beicheng Du, Fan Gao, Guangqi Zhang, Mingjie Ni, Xueqi Yacoby, Amir Cao, Yuan Fan, Shanhui Mazur, Eric School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States

Our MEMS-based twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, offering high-accuracy, multidimensional light manipulation. This finding holds potential for applica... 详细信息

ISBN: (纸本)9781510685048

关键词： Nanophotonics

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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