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15th International Renewable Energy Congress, IREC 2025

作者： Elrashid, M. Rajab, Z. Elhashane, M. Younis, A. Gomez-Romero, J. Tnatin, Salah I.S Mohamed, F. Benghazi University Faculty if Engineering Electrical and Electronic Department Libya University of Granada Department of Computer Science and Artificial Intelligence Spain Omar Al-Mukhtar University Faculty of Engineering Electrical Department Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

This research investigates the modeling, design, and simulation of a microgrid with advanced energy management controller, developed using MATLAB/Simulink. This paper presents a comprehensive analysis, modeling, and simulation of a smart controller for a boost converter, using an artificial neural network (ANN) for maximum power point tracking (MPPT) to enhance power output. The study begins with a comparison between traditional MPPT techniques and the ANN-based approach. These MPPT strategies are compared in terms of their response to dynamic weather conditions (such as temperature and irradiance variations) and their ability to minimize oscillations around the maximum power point. The simulation results demonstrate that the ANN-based MPPT method outperforms the conventional P&O and IC methods, providing faster response times and higher output power. Additionally, the system's performance was evaluated under conditions of power shortage and during battery charging and discharging cycles, showing the effectiveness of the smart controller in maintaining system stability and optimizing energy management. © 2025 IEEE.

关键词： MATLAB

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Effective Node Deployment in Wireless Sensor Networks Using Reinforcement Learning 14

Effective Node Deployment in Wireless Sensor Networks Using ...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Teja, Panduranga Ravi Vishwakarma, Anish Kumar Ranjan, Rakesh Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798331531935

Wireless Sensor Networks (WSNs) are vital for applications like environmental monitoring and smart cities, but effective node deployment remains challenging. Traditional methods often fail in dynamic scenarios. This paper proposes a Reinforcement Learning (RL)-based framework to optimize WSN node deployment, maximizing coverage while minimizing energy use and extending network lifetime. The RL model adapts to environmental changes and varying network conditions, ensuring robust performance in static, dynamic, and high-density environments. Experimental results show the RL approach outperforms conventional methods, achieving better coverage, lower energy consumption, and improved network longevity. The study demonstrates RL’s adaptability and effectiveness in addressing WSN deployment challenges, enabling intelligent, self-optimizing networks for dynamic real-world applications. © 2025 IEEE.

关键词： Reinforcement learning

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The impact of backbone selection in YOLOv8 models on brain tumor localization

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Iran Journal of computer science 2025年 1-23页

作者： Ranjbarzadeh, Ramin Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

This study investigates the utilization of the You Only Look Once (YOLOv8) deep learning framework for accurately identifying the location of brain tumors in medical imaging. We investigate the effects of model size and pretraining on the accuracy and computational efficiency of tumor detection by utilizing different setups of the YOLOv8 model. These setups include various configurations, ranging from very small to large, and can be pretrained on the COCO dataset or not. The experimental results, carried out on Google Colaboratory using NVIDIA Tesla T4 GPUs, show that pretrained models often achieve better performance by utilizing the extensive feature representations learned from the COCO dataset, resulting in increased precision in tumor location. For instance, the YOLOv8-XS model pretrained on COCO achieves an IoU of 0.278 and a Dice coefficient of 0.435, whereas its non-pretrained counterpart attains only 0.241 IoU and 0.388 Dice, indicating a 15% improvement in tumor localization accuracy. Similarly, pretrained YOLOv8-L achieves 0.269 IoU, outperforming standard object detection models such as Mask R-CNN (IoU: 0.212) and Faster R-CNN (IoU: 0.228). These results highlight the impact of pretraining on model performance, particularly for lightweight architectures, while also revealing diminishing returns for larger models. The research uncovers a subtle connection between the complexity of the model, pretraining, and the time required for training. It emphasizes the possible advantages and constraints of pretraining for various sizes of models. © The Author(s) 2025.

关键词： Brain tumor localization COCO dataset Deep learning Object detection Transfer learning

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Artificial sensory neurons and their applications

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Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

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Multi Meta Information Fusion Graph Neural Networks for Session-based Recommendation

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 366-382页

作者： Wen, Yan Sun, Hui-Zheng Liu, Liang Zhou, Ya-Kun Bian, Wei Yan, Ming-Hai College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Shandong Port Group Co. Ltd Qingdao266000 China Geography and Environment Management Faculty of Environment Universoty of Waterloo WaterlooN2L3G1 Canada

Session-based recommendation aims to predict the anonymous user’s next click action based on his/her latest click sequence. Most state-of-the-art approaches use graph neural networks to model sessions as graphs to capture dynamic transitions between items within a session. Inspired by recent progress on hypergraph-based neural networks, we think it is a good candidate to model the commonly related items within a session, and it can provide a sound complement to the normal graph models. In addition, cross session information may also be helpful. But the excessive noise introduced by other sessions is a big challenge and is still not be well resolved by existing works. To this end, we propose a new approach, called Multi Meta Information Fusion Graph Neural Networks (MM-GNN) to infer user preferences for the current session in a more comprehensive way for the anonymous *** fuse information from normal graph and hypergraph to fully exploit their capabilities of capturing sequential behavior and group intention within a session. In order to better use cross session information, we propose a globallevel subsequent item injection module, which injects information of the most possible subsequent item of the last item in the current session in the global scope. Besides, to learn richer information on the hypergraph structure, we model the whole session as a hyperedge to complement sliding windows-based hyperedges when creating the hypergraph. We have evaluated the method on several benchmark datasets and the results show that our method outperforms state-of-the-art methods. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Graph neural networks

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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Integrated Data Representation and Formal Analysis for Human and In Silico Experimentation in Cognitive Psychology 11th

Integrated Data Representation and Formal Analysis for Hum...

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11th International Symposium on From Data to Models and Back, DataMod 2023

作者： Cerone, Antonio Pluck, Graham Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Faculty of Psychology Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9783031872167

This paper presents a novel framework aimed at enhancing the experimental methodology within cognitive psychology, integrating both informal and formal components to accommodate varied reasoning approaches. The framework supports the design and performing of experiments, and the structured storage of the information on outcomes in a way that is independent of specific interpretations and findings. This facilitates the replication and reproduction of the experiments and their interpretation under alternative theories. Moreover, the formal components of the information stored can be used for in silico simulation as well as within rigorous analytical approaches, such as formal methods and process mining. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： BRDL Cognitive psychology Data storage Experiment builder Experimental variables Formal methods In silico simulation Process mining Real-Time Maude

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A Novel Pressure Sensor with Composite Sensitive Film for High Precision Measurement at Broad Range 38

A Novel Pressure Sensor with Composite Sensitive Film for Hi...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chen, Xiaopeng Ling, Sijia Tong, Hanyang Xiao, Yujing Zhang, Jin Yu, Zhengyin Jin, Qinghui Ningbo University The Faculty of Electrical Engineering and Computer Science China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology China

ISBN: (纸本)9798331508890

Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip size. This paper presents a tiny 0.6mm×0.8mm piezoresistive pressure sensor that offers exceptional sensitivity (0.27mV/kPa/3.3V) at a wide range of pressures (0-1MPa). The innovative composite pressure-sensitive film of the sensor is composed of a 10μm "*"-shaped single-crystalline silicon structure and a remarkably thin thickness of only 2.5μm poly-silicon film. It is developed utilizing the thin-film under bulk-silicon technique. And simultaneous improvement of detection sensitivity and linearity is achieved by etching out the nonlinear deformation region of the film. © 2025 IEEE.

关键词： Pressure sensors

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Multimodal Evidential Learning for Open-World Weakly-Supervised Video Anomaly Detection

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IEEE Transactions on Multimedia 2025年 27卷 3132-3143页

作者： Huang, Chao Huang, Weiliang Jiang, Qiuping Wang, Wei Wen, Jie Zhang, Bob Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518000 China University of Macau PAMI Research Group Department of Computer and Information Science China Ningbo University Faculty of Information Science and Engineering Ningbo China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen518055 China

Efforts in weakly-supervised video anomaly detection center on detecting abnormal events within videos by coarse-grained labels, which has been successfully applied to many real-world applications. However, a significant limitation of most existing methods is that they are only effective for specific objects in specific scenarios, which makes them prone to misclassification or omission when confronted with previously unseen anomalies. Relative to conventional anomaly detection tasks, Open-world Weakly-supervised Video Anomaly Detection (OWVAD) poses greater challenges due to the absence of labels and fine-grained annotations for unknown anomalies. To address the above problem, we propose a multi-scale evidential vision-language model to achieve open-world video anomaly detection. Specifically, we leverage generalized visual-language associations derived from CLIP to harness the full potential of large pre-trained models in addressing the OWVAD task. Subsequently, we integrate a multi-scale temporal modeling module with a multimodal evidence collector to achieve precise frame-level detection of both seen and unseen anomalies. Extensive experiments on two widely-utilized benchmarks have conclusively validated the effectiveness of our method. The code will be made publicly available. © 1999-2012 IEEE.

关键词： Self-supervised learning

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