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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

Alexandria engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

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A Lightweight Method to Survey with Protecting Privacy yet Maintaining Accuracy 18th

A Lightweight Method to Survey with Protecting Privacy yet...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yuxuan Di, Xinyu Liu, Ningbo Zhang, Xianchao Ren, Wei School of Computer Science China University of Geosciences Wuhan China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada International College Chongqing University of Technology Chongqing China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China Ministry of Education Chengdu China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China

ISBN: (纸本)9783031714665

In a survey, a lot of questions and answers are listed and participators will select one of the answers for each question. Sometimes, participators may not be willing to select the preferred one item in the answers because the item may damage the privacy of themselves. If this question is not answered, then it reveals that the question or the answers are sensitive, which also damages the privacy. Besides, each participator may have distinct privacy concerns even for the same questions and answers, thus the privacy-aware questions and answers cannot be determined and be removed in advance. Thus, the dilemma is how to select the answers for privacy-aware questions yet maintain the utility of the answering results in the survey. In this paper, we propose a general method to protect the various privacy in the investigation without the knowledge of privacy to be protected in advance. The main idea is that participators can randomly select answers if the privacy is the concern for themselves in the answers for this question. Our method is lightweight so as to be convenient in practices. We extensively analyze the accuracy of survey results to justify that our method can protect the privacy yet maintain the utility of the investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Easy-to-Fabricate High-Gain Linearly Polarized Antenna with Tunable Beam Pattern

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2025年

作者： Yerrola, Anil Kumar Ali, Maifuz Arya, Ravi Kumar Murmu, Lakhindar Kumar, Ashwani 1Department of Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh 493661 India 2Xiangshan Laboratory Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China 3School of Engineering Jawaharlal Nehru University Delhi New Delhi India

This paper presents an easy-to-fabricate, high-gain, linearly polarized antenna using a circular ground plane. The ground plane is made of a dielectric substrate sandwiched by two conductive sheets placed on the open end of a standard rectangular open-ended waveguide (OEWG). By modifying the aperture of OEWG, the gain of the radiating system is enhanced. Two methods are employed for such modifications, both of which deal with creating slots on the radiating front side of the conductive sheet to expose the dielectric substrate of the ground plane. In the first method, slots are introduced based on the even Fresnel zones, and their widths are optimized sequentially. This method increases the gain of the standard WR-90 waveguide from 6.86 dBi to 14.77 dBi. In the second method, surface current distributions are analyzed, and slots are introduced on the minimal surface current zones, which increases the gain to 16.59 dBi. The gain bandwidth product and 3 dB gain bandwidth are compared with the measured results. Measured results show good agreement with the Ansys HFSS simulated results. Next, an elliptical ground plane consisting of a dielectric substrate sandwiched between two conducting layers and elliptical slots is used to generate symmetric beam patterns of the E-and H-plane on the front side of the TE10 waveguide (WR-28). The modified Fresnel zone theory helped modify the circular ground plane to an elliptical ground plane and identify the elliptical slots. The introduced elliptical slots that helped generate the symmetrical beam pattern in the range of -45°≤θ≤45° are introduced based on the modified Fresnel zone equation. Lastly, a modification of the edge along the E-plane helped generate a symmetrical beam pattern in the range of -90°≤θ≤90°. © 2020 IEEE.

关键词： Antenna grounds

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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning

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Discover Applied Sciences 2025年第6期7卷 1-34页

作者： Bijoy, Md. Hasan Imam Mia, Md. Jueal Rahman, Md. Mahbubur Arefin, Mohammad Shamsul Dhar, Pranab Kumar Shimamura, Tetsuya Department of Computer Science and Engineering Daffodil International University Dhaka Daffodil Smart City1216 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama338-8570 Japan

Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnostics can assist nephrologists in early detection and accurate classification, which are essential for improving patient outcomes. This study utilized clinical features of CKD to develop and evaluate six base machine learning classifiers (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron) alongside two novel ensemble models (MKR Stacking and MKR Voting) for CKD prediction and classification. The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, information Gain, and Relief. The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. Such integration has the potential to transform CKD management, facilitating early interventions and personalized treatment plans through advanced machine-learning applications. © The Author(s) 2025.

关键词： k-nearest neighbors

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Satellite-UAV Networks for 6G Control: A Sensing-Communication-Computing-Control Closed Loop Perspective

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IEEE Network 2025年

作者： Lei, Chengleyang Fang, Xinran Feng, Wei Chen, Yunfei Xiao, Ming Ge, Ning Mao, Shiwen The Department of Electronic Engineering State Key Laboratory of Space Network and Communications Tsinghua University Beijing100084 China The Department of Engineering University of Durham DurhamDH1 3LE United Kingdom The Department of Information Science and Engineering School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The future sixth-generation (6G) networks are envisioned to support machines and robots besides human beings. In order to efficiently support machines and robots working in remote and post-disaster areas, satellites and unmanned aerial vehicles (UAVs) can be utilized. In this article, we investigate how satellite-UAV networks can serve robots for their control tasks. We introduce the typical use cases of the satellite-UAV networks for control and outline the relevant challenges. The control process usually involves sensing, communication, computing, and control components, which forms a sensing-communication-computing-control (SC3) closed loop. In analogy with the reflex arc structure of humans, we reveal the indivisibility of the SC3 closed loops and propose a closed-loop-oriented system design framework. Within this framework, we take the closed-loop control performance as the design target, and highlight the holistic consideration of sensing, communication, computing, and control components in the SC3 closed loop. Furthermore, we discuss the extended SC3 closed loop with multiple sensors and robots to decompose complex control systems. A case study is provided to show the performance gain of the proposed closed-loop-oriented system design. Finally, we outline open issues in designing more efficient satellite-UAV networks for control. ©1986-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Detecting Human-to-AI Author Change in Arabic Text

Detecting Human-to-AI Author Change in Arabic Text

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Amal Boutadjine Fouzi Harrag Mouad Bensouilah Sabrina Karboua Mohamed Deriche Department of Computer Science Faculty of science Setif 1 University-Ferhat ABBAS Setif Algeria Department of Computer Science University of Jijel Jijel Algeria AIRC College of Engineering and Information Technology Ajman University Ajman UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent large language models (LLMs), such as Gemini and ChatGPT, have demonstrated the ability to produce texts that are fluent and human-like when given precise instructions, presenting significant challenges in distinguishing between human-authored and AI-generated content, particularly in morphologically rich languages like Arabic. Despite the fact that these issues have prompted several research on AI content identification, the majority of these earlier investigations framed the problem as a binary classification problem, supposing that a text is either wholly AI-generated or totally human-written. This study introduces a novel methodology for detection where the text to be identified is cowritten by generative LLMs and humans, by identifying transitions in text authorship using advanced machine learning and deep learning techniques. We propose Trans-Detect a sophisticated neural architecture that combines AraBERT with a bidirectional LSTM network and a specialized attention mechanism, specifically designed to capture subtle linguistic variations in hybrid texts. Alongside traditional Random Forest, XGBoost, and LSTM-CNN models, our detector processes Arabic text datasets to identify and predict authorship transitions. The research achieved a macro F1-score of 0.6-0.8 with traditional models, while our proposed neural architecture demonstrated superior performance with an F1-score of 0.98, showing significant improvement in detecting text origin segments and revealing the complex nature of authorship changes. The findings provide a foundation for future research exploring the integration of advanced natural language processing techniques to enhance the accuracy and robustness of style change detection systems, particularly in handling the characteristics of Arabic text.

关键词： Accuracy Text detection Detectors Writing Linguistics Predictive models Feature extraction Transformers Robustness Random forests

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Incorporating Onboard Visual Sensing and Geographic information for Lane-Level Vehicle Localization

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IEEE Sensors Journal 2025年

作者： Lin, Huei-Yung Li, Jun-Yi Su, Ming-Wei National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering 168 University Road Chiayi621 Taiwan

With the recent advances in deep learning and artificial intelligence, vehicular technologies are progressing rapidly toward fully autonomous driving. Critical components of advanced driver assistance systems (ADAS) have been extensively investigated in the past decade. In this paper, we address the challenge of precise vehicle localization using visual perception and geographic information. The proposed method incorporates drivable area detection, lane line detection, and image-map matching to achieve lane-level localization accuracy. It takes advantage of digital maps and images from the onboard cameras to simultaneously optimize vehicle localization and lane detection. A confidence estimation is proposed to generate virtual lane lines for the lane marking identification. Our networks are pre-trained on the Cityscapes, CULane and TuSimple datasets, and fine-tuned with the newly proposed TRoad and TLane. In the experiments, the performance evaluated using MAE and RMSE have demonstrated a significant improvement over the conventional GPS/GNSS-based vehicle localization techniques. Source code and datasets are available at https://***/Wally0924/Enhancing-Vehicle-Localization-Using-Lane-Detection-and-Geographic-information. © 2001-2012 IEEE.

关键词： Advanced driver assistance systems

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MoCo: Urban User Mobile Contact Detection Based on Cellular Signaling Trace

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IEEE Transactions on Mobile Computing 2025年

作者： Duan, Sijing Lyu, Feng Zhang, Jing Lu, Huali Yang, Peng Wu, Huaqing Zhang, Yaoxue Shen, Xuemin Central South University School of Computer Science and Engineering Hunan ChangSha410083 China Tsinghua University Department of Computer Science and Technology Beijing China Huazhong University of Science and Technology School of Electronic Information and Communications Hubei Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada Tsinghua University Department of Computer Science and Technology Haidian District Beijing100084 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Mobile contact exhibits user co-traveling events within the same transportation tool, which is crucial for resident profiling, face-to-face interaction detection, etc. In this paper, we investigate urban user mobile contact detection with cellular signaling traces, which is cost-efficient to enable large-scale detection. Specifically, we develop a data collection platform to collect substantial user signaling traces, covering different types of road scenarios within a city. With the collected traces, we perform systematic data analysis to reveal several technical challenges, which are sparsity of signaling trajectory, remote base station noise, and fuzzy matching difficulties. To address challenges, we propose a mobile contact detection method named MoCo. In MoCo framework, we first conduct data denoising to remove the noise from remote base stations. Then, we devise a spatio-temporal filter to eliminate unlikely mobile contact traces in both spatial and temporal domains, reducing the computational overhead. Finally, we design a detection network that integrates the submodules of data alignment, feature encoder, spatio-temporal representation learner, and user mobile contact detector. Extensive evaluation results demonstrate the superiority of MoCo in comparison with state-of-the-art baselines. Robust experiments show that MoCo can work efficiently in different transportation modes and urban densities. © 2002-2012 IEEE.

关键词： Urban transportation

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Joint Optimization of Communication Latency and Platoon Control Based on Uplink RSMA for Future V2X Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Gang Hu, Jiewen Ma, Zheng Fan, Pingzhi Yu, F. Richard Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As an effective solution to address road congestion and improve traffic efficiency, vehicle platooning has received a lot of attention in recent years. However, vehicle-to-vehicle (V2V) communication latency can largely impact on the performance of vehicle platooning, which has not been well investigated. Rate-splitting multiple access (RSMA), as one of the promising technologies for 6 G, holds great potential for application in vehicle platooning to reduce the communication latency and thereby enhance platoon control performance. Motivated by the above considerations, platoon control and communication latency are jointly considered in this paper. We first introduce the communication latency into the vehicle dynamics model, and then derive a distributed model predictive control (DMPC)-based platoon control model. To reduce the communication latency, uplink RSMA is introduced for inter-platoon V2V communication. Since the latency minimization problem is non-convex and challenging to solve directly, we first employ a bisection method to give a latency upper bound and transform the initial problem into a non-convex feasibility problems in each iteration. Subsequently, through theoretical derivation, the bandwidth allocation factor is expressed in terms of transmission power, which reduces the number of optimization variables and transforms the problem into a convex problem. Then we can obtain the optimal solution by solving a convex problem in each iteration. Finally, the simulation results demonstrate that the proposed RSMA significantly outperforms traditional frequency division multiple access (FDMA) and non-orthogonal multiple access (NOMA), and can significantly reduce communication latency, enhance platoon control safety, improve driving comfort, and lower the performance requirement on vehicle maximum torque. © 2025 IEEE.

关键词： Traffic congestion

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