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IAENG International Journal of computer science 2025年第5期52卷 1325-1335页

作者： Li, Yuanyuan Wu, Jiansheng Liu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China University of Science and Technology Liaoning Anshan114051 China

Retinal vessel image segmentation is crucial for the early diagnosis and treatment of ophthalmic diseases. However, accurate segmentation remains challenging due to the complexity and diversity of retinal images, as well as the intricate nature of vascular structures. Existing methods often underperform when dealing with fine vessels and complex backgrounds, limiting their effectiveness in clinical applications. This study proposes a novel retinal vessel image segmentation method, Dual Attention FR-UNet (DA-FR-UNet), which incorporates two innovative attention mechanisms: Enhanced Channel Prior Attention (ECPA) and Hard Triplet Attention (HTA). The ECPA module enhances the model's ability to recognize fine blood vessels by integrating global average pooling and global maximum pooling to effectively capture multi-scale features. In contrast, the HTA module improves the model's understanding of complex vascular structures by simultaneously considering attention information across three dimensions: channel, spatial height, and width. Additionally, the study introduces the testtime augmentation (TTA) technique during the experimental phase to enhance the model's generalizability and robustness. To validate the performance of the DA-FR-UNet model in retinal vessel image segmentation tasks, a series of comparative experiments were conducted on the FIVES and RVD datasets. The experimental results demonstrate that the DA-FR-UNet model, combining the dual-attention mechanism and the TTA strategy, outperforms existing state-of-the-art methods across several key evaluation metrics. On the FIVES dataset, DAFR- UNet achieves a sensitivity of 88.42%, an IoU value of 82.27%, and a Dice coefficient of 89.72%. On the RVD dataset, these metrics are 65.46%, 44.53%, and 61.43%, respectively. These findings highlight the model's significant advantages in processing fine vessels and complex backgrounds, improving its sensitivity and robustness in blood vessel image segmentation. © (2025)

关键词： Image segmentation

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

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Tsinghua science and Technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

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Design of High-Power Power Amplifiers for BDS-3 Terminal Based on InGaP/GaAs HBT MMIC and LGA Technology 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Li, Zhenbing Jia, Shilin Zhang, Jinrong Fu, Jialong Huang, Junjie Fang, Xiaochuan Li, Gang Wen, Guangjun School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Antennas and Electromagnetics Research Group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

With the development and popularization of Beidou-3 navigation satellite system (BDS-3), to ensure its unique short message function it is necessary to integrate a radio frequency (RF) power amplifier (PA) with high performance. Therefore, in this paper, an L-band high-power 10 W PA chip is designed in InGaP/GaAs heterojunction bipolar transistor (HBT) technology combined with temperature-insensitive adaptive bias technology, class-F harmonic suppression technology, and LGA packaging technology. The linear gain of the PA chip reaches 37.1 dB and P1dB reaches 40.6 dBm. The results show that the PA chip has high power, high gain, and high linearity, which has obvious advantages over similar PA chip designs and can meet the short message functions of the BDS-3 terminals in various application scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Power amplifiers

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification 24

SAM-MIL: A Spatial Contextual Aware Multiple Instance Learni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Fang, Heng Huang, Sheng Tang, Wenhao Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current MIL models predominantly rely on instance-level features derived from pretrained models such as ResNet. These models segment each WSI into independent patches and extract features from these local patches, leading to a significant loss of global spatial context and restricting the model's focus to merely local features. To address this issue, we propose a novel MIL framework, named SAM-MIL, that emphasizes spatial contextual awareness and explicitly incorporates spatial context by extracting comprehensive, image-level information. The Segment Anything Model (SAM) represents a pioneering visual segmentation foundational model that can capture segmentation features without the need for additional fine-tuning, rendering it an outstanding tool for extracting spatial context directly from raw WSIs. Our approach includes the design of group feature extraction based on spatial context and a SAM-Guided Group Masking strategy to mitigate class imbalance issues. We implement a dynamic mask ratio for different segmentation categories and supplement these with representative group features of categories. Moreover, SAM-MIL divides instances to generate additional pseudo-bags, thereby augmenting the training set, and introduces consistency of spatial context across pseudo-bags to further enhance the model's performance. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that our proposed SAM-MIL model outperforms existing mainstream methods in WSIs classification. Our open-source implementation code is is available at https://***/FangHeng/SAM-MIL. © 2024 ACM.

关键词： Self-supervised learning

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Making Smart Contract Classification Easier and More Effective 15

Making Smart Contract Classification Easier and More Effecti...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Zhu, Zhirong Su, Jianzhong Jiang, Zigui Ye, Mingxi Zheng, Zibin School of Computer Science and Engineering Sun Yat-sen University Taiwan School of Software Engineering Sun Yat-sen University Taiwan

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Nowadays, as an emerging and powerful technology, smart contract supports decentralized trusted computing and is widely used in various business fields such as finance, exchange and game. Although smart contracts provide a convenient way to manage funds in the business, they also bring economic loss to users. Particularly, there are a huge number of malicious smart contracts running on blockchain, which defraud users of money. Therefore, identifying the intention of smart contracts is of great significance, which will help users have a better understanding of smart contracts and reduce their financial losses. In this paper, we purpose a supervised classification method to detect the intention of smart contracts. Specifically, we extract the features of smart contracts from codes and transactions, and then use XGBoost to classify smart contracts. We conduct extensive experiments on on-chain smart contracts. The experimental results demonstrate that our features and model are capable of effectively identifying the types of smart contracts. © 2022 IEEE.

关键词： Smart contract

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Cost-Optimized Microservice Deployment for IoT Application in Cloud-Edge Collaborative Environment 26

Cost-Optimized Microservice Deployment for IoT Application i...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Xiaoyuan Tang, Bing Yang, Qing Xu, Wei Guo, Feiyan Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou510725 China

ISBN: (纸本)9798350331684

With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires suitable strategies to ensure the quality of service for users. However, the existing container orchestration tools cannot flexibly select the best deployment location according to the user's cost budget, and are insufficient in personalized deployment solutions. From the perspective of application providers, this paper considers the location distribution of users, application dependencies, and server price differences, and proposes a genetic algorithm-based Internet-of-Things (IoT) application deployment strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints. This problem is an NP-hard problem, and genetic algorithm is introduced to solve the optimization problem effectively and improve the deployment efficiency. The proposed algorithm is compared with four baseline algorithms, Time-Greedy, Cost-Greedy, Random and PSO, using real datasets and some synthetic datasets. The results show that the proposed algorithm outperforms other competing baseline algorithms. © 2023 IEEE.

关键词： Genetic algorithms

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Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model Contrastive Detection

Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoyun Gan Peng Lu Shanyu Gan Youqian Xian Kaichen Peng Peng Liu Dongcheng Li School of Computer Science and Engineering/School of Software Guangxi Normal University Guilin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL particularly susceptible to attacks. A pivotal challenge within FL is the defense against Byzantine attacks, which can alter the model aggregation process and ultimately lead to the failure of Federated Learning convergence. Existing Byzantine defense mechanisms often result in diminished model accuracy in scenarios characterized by non-independent and identically distributed (non-IID) data. To alleviate this issue, we propose a novel defense approach named Fed-BRMC. This method combines a self-developed federated Dual-Model Contrastive Detection technique with Similarity Detection methods, leveraging historical update information from users to deeply mine the characteristics of model parameters, effectively distinguishing between Byzantine and honest users. Extensive experimentation has shown that Fed-BRMC significantly improves user identification accuracy in non-IID data scenarios compared to existing methods and notably enhances the global model accuracy of Byzantine-robust Federated Learning.

关键词： Training Degradation Accuracy Federated learning Distributed databases Collaboration Data models Security Cybernetics Convergence

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