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International IoT, Electronics and Mechatronics Conference, IEMTRONICS 2024

作者： Ammari, Ahmed Chiheb Al Hmouz, Rami Khriji, Lazhar Zhou, MengChu Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9789819747832

The number of Distributed Green Cloud Datacenters (DGCDs) is globally increasing. Such DGCDs deploy different types of renewable sources to generate clean energy and save money. They are located in different regions depending on the availability of renewable energy sources, costs of bandwidth and grid electricity prices. This paper focuses on applications within them, which are sensitive to delay and looks into a way to schedule different applications with respect to time constraint. The paper uses the Firefly algorithm, bat algorithm and simulated annealing-bat algorithms as optimization techniques to minimize total operational cost of such DGCDs. These algorithms have been compared through data-driven experiments carried out in this study. Of particular note is the Firefly algorithm's superior performance when compared with others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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Two-Stage Target Detection for Compact HFSWR With Space-to-Depth YOLOv8 and Multiframe ViT

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10442-10457页

作者： Wu, Tong Li, Ming Niu, Jiong Zhang, Ling Zhang, Wandong Wu, Qing Ming Jonathan Ocean University of China College of Engineering Qingdao266100 China Western University Department of Electrical and Computer Engineering LondonONN6A 3K7 Canada University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

Accurate and reliable target detection is a crucial requirement of high-frequency surface wave radar for effective maritime surveillance. However, existing methods based on single-frame radar images primarily focus on static target features, limiting their ability to capture dynamic ship behaviors across multiple frames. In this study, a two-stage multiframe target detection framework (MFTDF) is proposed to address this issue in ship-target detection from range-Doppler (RD) images. The framework consists of two stages: a space-to-depth YOLOv8 network in Stage-1 and a multiframe vision transformer network in Stage-2. First, Stage-1 aims to extract regions of interest (ROI) from the current and several preceding RD images. Thereafter, target association and pattern selection are applied to collect multiframe image patch sequences for each ROI result. Finally, Stage-2 focuses on further discriminating the input image patches to obtain refined target detection results. Moreover, the dataset for training and validating the two-stage network is automatically generated based on the automatic identification system ship data and constant false alarm rate detection results to identify all true and visible targets, ensuring the dataset's credibility. Experiments using the measured data show that the proposed MFTDF achieves a considerably improved precision rate while maintaining an average improvement of 12.3% in the recall rate. These results confirm that MFTDF delivers superior detection accuracy and efficiency, offering a robust solution for maritime target detection in complex scenarios. © 2008-2012 IEEE.

关键词： Doppler effect

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Strategies for Effective EEG Preprocessing 22

Strategies for Effective EEG Preprocessing

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22nd International Learning and Technology Conference, L and T 2025

作者： Salem, Nema Alamoudi, Nora Mustafa, Lama Bagais, Fatima Effat University Electrical Research Laboratory College of Engineering Jeddah Saudi Arabia Effat University College of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology;however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produces superior outcomes compared to other techniques investigated, achieving a high Signal-To-Noise Ratio (SNR) of 43.23 dB. © 2025 IEEE.

关键词： Anesthesia Discrete Wavelet Transform EEG preprocessing Gaussian Independent Component Analysis Moving average Noise removal SDG 17

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Deep learning and vision transformers-based framework for breast cancer and subtype identification

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Neural computing and Applications 2025年 1-20页

作者： Jahan, Ishrat Chowdhury, Muhammad E. H. Vranic, Semir Al Saady, Rafif Mahmood Kabir, Saidul Pranto, Zahid Hasan Mim, Sabiha Jahan Nobi, Sadia Farhana Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Department of Electrical Engineering Qatar University 2713 Doha Qatar College of Medicine QU Health Qatar University Doha Qatar Department of Electrical and Electronic Engineering University of Dhaka Dhaka1000 Bangladesh

Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer det

关键词： Diseases

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SYNAuG: Exploiting synthetic data for data imbalance problems

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Pattern Recognition Letters 2025年 193卷 115-121页

作者： Ye-Bin, Moon Hyeon-Woo, Nam Choi, Wonseok Kim, Nayeong Kwak, Suha Oh, Tae-Hyun Department of Electrical Engineering POSTECH Pohang Korea Republic of Grad. School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering POSTECH Pohang Korea Republic of School of Computing KAIST Daejeon Korea Republic of

Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synthetic data to equalize the unbalanced distribution of training data. Our experiments demonstrate that although a domain gap between real and synthetic data exists, training with SYNAuG followed by fine-tuning with a few real samples allows us to achieve impressive performance on diverse tasks with different data imbalance issues, surpassing existing task-specific methods for the same purpose. © 2025 Elsevier B.V.

关键词： Data augmentation Fairness Model robustness Synthetic data long-tailed recognition

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Artificial Intelligence and Machine Learning for Accessibility: Smart Reader as an Assistive Tool for the Visually Impaired 2

Artificial Intelligence and Machine Learning for Accessibili...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Palarimath, Suresh Radhakrishnan, Venkateswaran Veerasamy, Devarajan Valsalan, Prajoona Viswan, Vimbi Thumu, Muni Balaji College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sohar Oman

ISBN: (纸本)9798331505745

The amalgamation of artificial intelligence (AI) and machine learning (ML) has greatly improved accessibility for visually impaired individuals, fostering increased autonomy through pioneering assistive technology. This study introduces the Smart Reader, a device that integrates optical character recognition (OCR) and text-to-speech (TTS) technologies to enhance accessibility to printed documents. The Smart Reader utilizes a camera to take text images, translates them into digital representation through OCR, and converts the text into audible speech via TTS. The system has an intuitive user interface, an automatic page-turning mechanism, and a dictionary lookup function, guaranteeing a seamless reading experience. The findings indicate that the Smart Reader significantly improves access to written information, hence enhancing the quality of life for visually impaired individuals, especially in educational environments such as schools and libraries. The device is engineered for user-friendliness, enhancing accessibility and autonomy. The Smart Reader utilizes AI and ML, marking a significant progression in assistive technology, promoting an inclusive society where visually impaired individuals can engage with printed material with assurance. In addition to functionality, it seeks to empower people by connecting capability with opportunity, guaranteeing equitable access to information for everyone. © 2025 IEEE.

关键词： Contrastive Learning

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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An improved swarm intelligence for power system economic operations based on optimal power generation to control congestion in transmission channels

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Neural computing and Applications 2025年 1-28页

作者： Kumari, Pooja Paul, Kaushik Kumar, Niranjan Sinha, Pampa Agarwal, Krishna Kant Vidyarthi, Ankit Alkhayyat, Ahmed Department of Electrical Engineering NIT Jamshedpur Jamshedpur India Department of Electrical Engineering BIT Sindri Dhanbad828123 India School of Electrical Engineering KIIT University Bhubaneswar751024 India School of Computing Science and Engineering Galgotias University Greater Noida India Department of CSE & IT Jaypee Institute of Information Technology Uttar Pradesh Noida201309 India College of Technical Engineering The Islamic University Najaf Iraq

Power system congestion is a key challenge in the deregulated era. Finding ways to transmit power without congestion is crucial. Achieving optimal generator power output with the application of evolutionary algorithms is a primary method to manage this issue. This paper proposes a novel modified whale optimization algorithm (MWOA) for the optimal power generation to address the power system economic operations for the nonlinear transmission congestion management cost problem. The MWOA has been developed by introducing two correction factors in the exploration and exploitation phases that enhances its performance and coordination in these two phases. The MWOA strikes a good balance between exploring and exploiting WOA phases. Its capabilities are validated using benchmark functions and compared to other heuristic algorithms. The MWOA has been applied to accomplish minimum congestion cost by optimal adjustment in the generator power output while alleviating congestion in the lines. Generator sensitivity factors are computed to identify the most sensitive generators that are participating in the power rescheduling process. The results show that MWOA lowered congestion costs by 6.00%, 4.45%, 3.14%, and 2.30% for the 39-bus system, 7.91%, 4.29%, 2.57%,1.69% for the 30-bus system, and 10.44%, 8.48%, 7.36%, 5.17%, 2.00% for 118-bus system when compared to FEP, GA, DE, and original WOA. Statistical and comparative analysis has shown that with MWOA there has been a considerable reduction in the congestion cost, system losses, amount of power rescheduled, and improved in the system bus voltages when contrasted with other referred optimization algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Swarm intelligence

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Enhancing PCOS diagnosis accuracy with modified Giza Pyramids Optimizer, SLOGPO, and PCOSNet: advanced classification and optimization techniques

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Cluster computing 2025年第5期28卷 1-27页

作者： Aldosary, Abdallah El-Shafai, Walid Mohamed, Ehab Mahmoud Emara, Heba M. Department of Computer Engineering College of Engineering in Wadi Addwasir Prince Sattam bin Abdulaziz University Wadi Addwasir11991 Saudi Arabia Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering College of Engineering in Wadi Addawasir Prince Sattam Bin Abdulaziz University Wadi Addawasir11991 Saudi Arabia Department of Electronics and Electrical Communications Engineering High Institute of Electronic Engineering Ministry of Higher Education Bilbis-Sharqiya44621 Egypt

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women during their reproductive years, characterized by a range of hormonal imbalances and reproductive dysfunctions. Accurate diagnosis of PCOS is crucial, as it serves as the foundation for timely interventions and personalized management strategies. In this context, this paper presents a comprehensive evaluation of two advanced classification models: the Stacked Learning Optimized by Giza Pyramids Optimizer (SLOGPO) and PCOSNet, a model leveraging 1-Dimensional Convolutional Neural Networks (CNN) integrated with a BORUTA-based feature selection framework. The study employs three distinct datasets, each containing a variety of physical and clinical parameters. The SLOGPO model, which combines stacked learning, the Improved Giza Pyramids Optimizer (IGPO), and BORUTA for feature selection, demonstrated strong generalization capabilities, achieving accuracies of 96% on Dataset I, 96% on Dataset II, and 95% on Dataset III. In contrast, the PCOSNet model, based on a 1D CNN architecture with BORUTA, achieved accuracies of 94% on Dataset I, 93% on Dataset II, and 84% on Dataset III. These results underscore the superior generalization ability of the SLOGPO model across varied datasets, while the performance of PCOSNet indicates areas for further improvement, particularly in handling diverse datasets. This study provides valuable insights into the impact of feature selection techniques and dataset variability on the performance of PCOS detection models. The findings emphasize the importance of evaluating models across multiple datasets to assess their generalizability and robustness, laying the groundwork for future research in PCOS detection and related medical applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8 24

Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8

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7th International Conference on Information Science and Systems, ICISS 2024

作者： Ortega, Gerson Itliong Ordas, Amiel Mir Odiamar Villaverde, Jocelyn Flores Juanatas, Roben Antalan Juanatas, Irish Castrojeres School of Electrical Electronics and Computer Engineering Mapua University Philippines College of Computing in Information Technologies National University Philippines Department of Information Technology FEU Diliman Philippines

ISBN: (纸本)9798400717567

The advancement of YOLO and similar computer vision models represents a technological era that could revolutionize numerous aspects of day-to-day life, including traffic management. This study explores the feasibility of utilizing YOLOv8 for pedestrian detection and counting to control traffic light timing sequences at isolated pedestrian crossings. By employing the Raspberry Pi 4 Model B with a camera module, real-time pedestrian video data can be captured and processed through YOLOv8. For the hardware, three LEDs and a Dot Matrix Module replicate conventional traffic light signals and timing, effectively simulating a traffic light system. The capabilities of the custom YOLOv8 model are evaluated through pedestrian detection accuracy, calculated by percentage error across twenty samples. Results reveal an average percent error of 7.33%, indicating 92.67% accuracy in pedestrian detection and counting. These findings underscore the model’s competence for pedestrian-based adaptive traffic light control, highlighting its potential to enhance practicality and safety at pedestrian crossings by dynamically adjusting signal timings based on real-time pedestrian activity. © 2024 Copyright held by the owner/author(s).

关键词： Pedestrian safety

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