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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Performance of pre-trained deep learning models for land use land cover classification using remote sensing imaging datasets

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Environmental Earth sciences 2025年第11期84卷 1-36页

作者： Haider, Irfan Khan, Muhammad Attique Masood, Saleha Algamdi, Shabbab Ali Alasiry, Areej Marzougui, Mehrez Nam, Yunyoung Department of Computer Science HITEC University Taxila Pakistan Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia IRC for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Software Engineering College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of

This study presents a comparative analysis of ten pre-trained convolutional neural network (CNN) models, evaluated across three remote sensing datasets: EuroSat, NWPU, and Earth Hazards (Land Sliding). We investigate the interplay between model architecture and classifier selection by incorporating five different neural network (NN) classifiers, emphasizing their impact on predictive accuracy and computational efficiency. Due to its densely connected architecture, DenseNet201 achieved the highest accuracy—97% on EuroSat, 99.40% on NWPU, and 97.80% on Earth Hazards. In contrast, MobileNetV2, while slightly less accurate, demonstrated superior computational efficiency, recording the shortest prediction times of 39.943 s on EuroSat, 27.482 s on NWPU, and 2.8986 s on Earth Hazards. Additionally, classifier choice significantly influenced performance, with the Wide NN classifier excelling in diverse datasets and the Medium NN classifier optimizing speed. Our findings underscore the importance of balancing accuracy and efficiency in selecting CNN models for remote sensing applications, suggesting future research should explore ensembling techniques and lightweight models to enhance performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： software design

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Multi-perception large kernel convnet for efficient image super-resolution

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四川大学学报(自然科学版) 2025年第1期62卷 67-78页

作者： MIAO Xuan LI Zheng XU Wen-Zheng College of Computer Science(College of Software) Sichuan UniversityChengdu 610065China Tianfu Engineering-Oriented Numerical Simulation&Software Innovation Center Sichuan UniversityChengdu 610207China

Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art *** efforts have explored the incorporation of Transformers to augment network performance in ***,the high computational cost of Transformers makes them less suitable for deployment on lightweight ***,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel *** this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel ***,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these *** addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel *** results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.

关键词： Single Image Super-Resolution Lightweight model Deep learning Large kernel

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Blockchain-Based Forensic Investigation Metamodel (BBFIM) for Investigating IoT Cybercrimes

Blockchain-Based Forensic Investigation Metamodel (BBFIM) fo...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Daifallah Zaid Alotaibe Department of Software Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin Kingdom of Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

As smart devices are rapidly being developed, and people trust data transfer mechanisms such as Wi-Fi and the way they use them, as well as the fact that they rely heavily on data transfer mechanics, there has been an increase in cyber-attacks. Therefore, examining IoT safety threats and potential countermeasures can support investigators in determining how to deal with a range of stressful scenarios encountered when conducting cyber-crime studies to develop acceptable methods for dealing with them. The goal of this paper is to develop Blockchain-Based Forensic Investigation Metamodel for Investigating IoT Cybercrimes called (BBFIM). The purpose of the developed BBFIM is to improve the productivity and accuracy of examinations by providing a safe and decentralized environment where digital evidence, related to IoT-related crimes, can be stored, and analyzed to improve the productivity and accuracy of examinations. Through the integration of blockchain technologies the developed BBFIM ensures that the reliability of the evidence is retained throughout the investigation. It also provides transparency throughout the process. This study utilizes design science methods for development and validation purposes. Therefore, 889 IoT forensic models and frameworks have been reviewed in this study, and 15 models have been selected to develop the BBFIM. The developed BBFIM consists of three main IoT forensic practices: Forensic investigation phases, Forensic Tools and Techniques, and Blockchain Infrastructure. Compared to the existing IoT forensics models the developed BBFIM is fully decentralized which can guarantee the authority, consistency, and privacy of the data.

关键词： Productivity Accuracy Forensics Design methodology Data transfer Blockchains Safety Smart devices Cyberattack Wireless fidelity

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A Safety Evaluation Method and System for a High Core Wall Dam Based on Construction Data

Journal of Intelligent Construction

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Journal of Intelligent Construction 2025年第1期3卷 1-14页

作者： Zicheng Wang Yingchi Mao Yaosheng Tan Qiaoling Shen Houlei Xu College of Computer Science and Software Engineering Hohai University Nanjing China PowerChina Kunming Engineering Corporation Limited Kunming China China Three Gorges Group Corporation Beijing China

In view of the complex operational conditions and dynamically changing safety risks associated with high core wall dams in the construction period, a comprehensive safety evaluation system for high core wall dams, which utilizes fuzzy theory and emergency response methods, has been developed. Considering the material zoning, load characteristics, and layout of the safety monitoring system specific to high core wall dams and considering the impacts of flood control capacity, foundation conditions, structural safety, operational status, and slope conditions, a safety evaluation method for high core wall dams on the basis of construction data has been proposed. The model dynamically determines the weights of evaluation factors via evaluation criteria and the term frequency-inverse document frequency (TF-IDF) method. It constructs a comprehensive evaluation subordination feature vector, calculates the degree of match between the current safety evaluation result and historical safety evaluation results, and identifies the matching operational conditions. Finally, on the basis of the risk analysis matrix, the risk level of each tier of evaluation factors is determined, and emergency response measures are formulated. This system provides an online monitoring platform for the operational safety of high core wall dams. This approach enhances the capacity for safety analysis and risk emergency decision-making in hydraulic and hydroelectric engineering.

关键词： Matched filters Dams Current measurement Inspection Emergency services Vectors Floods Risk analysis Personnel Monitoring

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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A self-supervised graph convolutional model for recommendation with exponential moving average

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Neural Computing and Applications 2025年第9期37卷 6777-6793页

作者： Chen, Rui Pang, Kangning Wang, Zonglin Liu, Qingfang Tang, Cundong Chang, Yanshuo Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China Information Center Shijiazhuang Posts and Telecommunications Technical College Shijiazhuang050021 China School of Information Science and Technology Northwest University Xi’an710127 United States Insititute for Silk Road Research Xi’an University of Finance and Economics Xi’an710100 China

Recommendation algorithms based on graph convolutional networks can integrate user and item node information along with their interaction topology, better capturing the intricate relationships between users and items, thereby enhancing the accuracy of recommender systems. However, existing methods often overlook the impact of noise in user behavior data on recommendation performance. Additionally, when there are too many convolutional layers in the graph, the node representations tend to smoothing, resulting in an inability to accurately distinguish user preferences. To address these issues, we propose a self-supervised graph convolutional model for recommendation with exponential moving average (SGCERec). Specifically, we first employ exponential moving average (EMA) techniques from the field of time-series analysis to denoise the raw user interaction data. Then, by applying layer filtering technique to update the propagation of information and the representation of nodes within the graph convolutional network, we effectively deepen the model hierarchy, enabling the model to gain a deeper understanding of the features and structures of the graph data, thereby improving the performance and effectiveness of the recommender systems. Finally, experimental results on three real datasets show that SGCERec outperforms state-of-the-art recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Recommender systems

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A Comprehensive Survey on Communication-Efficient Federated Learning in Mobile Edge Environments

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IEEE Communications Surveys and Tutorials 2025年

作者： Jia, Ninghui Qu, Zhihao Ye, Baoliu Wang, Yanyan Hu, Shihong Guo, Song Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Nanjing211100 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Kowloon Hong Kong

In traditional centralized machine learning, transmitting raw data to a cloud center incurs high communication costs and raises privacy concerns. This is particularly challenging in mobile edge environments, where devices are dynamic and resource-constrained. Federated Learning (FL) addresses these issues by allowing devices to train models locally and upload parameters to a central server without sharing local data. However, limited wireless channel resources and dynamic transmission performance make communication overhead a major bottleneck of FL in mobile edge environments. Concerning this challenge, this survey provides a comprehensive summary of methods to improve communication efficiency in FL, focusing on: 1) minimizing communication complexity to reduce total transmission volume, 2) scheduling resources appropriately to improve training efficiency, 3) utilizing over-the-air computation (OTA) to integrate computation into communication for accommodating the computation/communication characteristics of FL in mobile edge environments. Thus, this work analyzes research from the perspective of convergence and data heterogeneity to reduce communication rounds by optimizing algorithm performance. We hope that this survey could offer insights into communication-efficient FL for future research. © 2025 IEEE.

关键词： Federated learning

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A review on machine learning techniques for network security

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Journal of Cyber Security Technology 2025年

作者： Nazir, Rashid Laghari, Asif Ali Dahri, Fida Hussain Shoulin, Yin Alhakeem, Zaineb M. Hakim, Heba Mughal, Zeeshan Ahmed Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan Software college Shenyang Normal University Shenyang China School of Computer Science and Engineering Southeast University Nanjing China Chemical Engineering and Oil Refining Department Oil and Gas Engineering College Basrah University for Oil and Gas Basrah Iraq Computer Engineering Department Engineering College University of Basrah Basrah Iraq

Network security encompasses the strategies and techniques to protect networks from unauthorized access and potential threats. Network security is essential to protect layers of networks and data transferring on them. Data integrity and the underlying network infrastructure are necessary in today’s digital landscape. This paper explores the role of machine learning (ML) in enhancing network security by developing intelligent tools and methodologies. We critically analyze various ML techniques utilized in this field, their practical applications, and the advancements achieved in fortifying networks against cyber threats. We address ML’s challenges and limitations in network security, concluding with a discussion of open research issues for future investigation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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MAP-SIM: A DNN-Specific Mapping Optimization Framework for Shared-Memory CPU-Systolic Array Architectures

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Yuhang Wen, Mei Shen, Junzhong Chen, Zhaoyun Shi, Yang Wang, Tianyu Shao, Zili National University of Defense Technology College of Computer Science and Technology Hunan Changsha410073 China Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

As performance demands continue to rise, Shared-Memory Heterogeneous Systems (SMHSs) have been widely adopted for their ability to enable efficient communication and data sharing between different heterogeneous cores. However, existing SMHS face challenges in uneven workload distribution among heterogeneous cores and suboptimal mapping schemes, preventing them from fully leveraging their architectural advantages. To address these issues, this paper proposes a mapping-aware framework for modeling SMHSs called MAP-SIM. By performing performance modeling for CPUs and Systolic Arrays (SAs), and considering rational schemes for the partition and mapping of computational tasks, MAP-SIM aims to evaluate and optimize the computational performance of heterogeneous multicore architectures. The experimental results show that compared to previous work, MAP-SIM can increase simulation speed by 14 to 67 times and can also enhance the computational performance of SMHS by 1.4 to 4.4 times. © 1982-2012 IEEE.

关键词： Photomapping

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