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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13417 条记录，以下是4921-4930 订阅

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Bio-inspired Intrusion Detection System for Internet of Things Networks Security

Bio-inspired Intrusion Detection System for Internet of Thin...

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2024 Cognitive Models and Artificial Intelligence conference, AICCONF 2024

作者： Harbi, Yasmine Merat, Salsabyl Aliouat, Zibouda Harous, Saad Ferhat Abbas University Setif Algeria Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400716928

The contemporary era has witnessed substantial growth in the Internet of Things (IoT), accompanied by an escalation in data volume due to the proliferation of Internet-connected devices. Unfortunately, this surge has attracted the attention of cybercriminals, who are now targeting IoT networks for malicious activities. Consequently, security and privacy concerns have emerged as the primary obstacles impeding the widespread adoption of IoT. While complete prevention of attacks on any system is not feasible, real-time detection of such attacks is crucial for effectively safeguarding IoT systems. This paper introduces a novel Intrusion Detection System (IDS) that employs supervised machine learning and bio-inspired algorithms to identify security anomalies in IoT networks. The IDS scrutinizes all dataset features to detect intrusive data and trains itself to predict potential network intrusions. However, some features may be extraneous or unrelated to the detection process, resulting in computational complexities and increased detection time. To tackle this issue, we implemented a feature selection process using a modified firefly algorithm to eliminate irrelevant features from the dataset and choose the optimal subset of features (reducing from 74 to 39). This optimization enhances the performance of the IDS by decreasing detection time and improving prediction accuracy. The obtained results affirm that the proposed intrusion detection system is capable of identifying real-world intrusions and can serve as an effective security solution for IoT systems. © 2024 ACM.

关键词： Decision trees

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Federated learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram data 24

Federated Learning and Differential Privacy Techniques on Mu...

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8th international conference on Medical and Health Informatics, ICMHI 2024

作者： Vikhyat, Agrawal Sunil, Kalmady Venkataseetharam, Manoj Malipeddi Manisimha, Varma Manthena Weijie, Sun Islam, Saiful Abram, Hindle Padma, Kaul Russell, Greiner Canadian VIGOUR Centre University of Alberta Canada Department of Physics IIT Bombay India Department of Computing Science University of Alberta Canada Canadian VIGOUR Centre Department of Medicine University of Alberta Canada Department of Computer Science and Engineering IIT Kharagpur India

ISBN: (纸本)9798400716874

This research paper explores ways to apply Federated learning (FL) and Differential Privacy (DP) techniques to population-scale Electrocardiogram (ECG) data. The study learns a multi-label ECG classification model using FL and DP based on 1,565,849 ECG tracings from 7 hospitals in Alberta, Canada. The FL approach allowed collaborative model training without sharing raw data between hospitals, while building robust ECG classification models for diagnosing various cardiac conditions. These accurate ECG classification models can facilitate the diagnoses while preserving patient confidentiality using FL and DP techniques. Our results show that the performance achieved using our implementation of the FL approach is comparable to that of the pooled approach, where the model is trained over the aggregating data from all hospitals. Furthermore, our findings suggest that hospitals with limited ECGs for training can benefit from adopting the FL model compared to single-site training. In addition, this study showcases the trade-off between model performance and data privacy by employing DP during model training. Our code is available at https://***/vikhyatt/Hospital-FL-DP. © 2024 ACM.

关键词： Federated learning

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Deep Interpretable Component Decoupled Dictionary Neural Network for Image Denoising in Industrial Cyber-Physical System 16

Deep Interpretable Component Decoupled Dictionary Neural Net...

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international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart data, Smartdata and Congress on Cybermatics (Cybermatics)

作者： Deng, Lizhen Pan, Yushan Xu, Guoxia Yan, Taiyu Wang, Zhongyang Zhu, Hu Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing Peoples R China Xian Jiaotong Liverpool Univ Dept Comp Suzhou Peoples R China Xidian Univ Sch Cyber Engn Xian Peoples R China

ISBN: (纸本)9798350309461

Image denoising techniques are pivotal in preprocessing noisy images, greatly enhancing the quality of visual data in applications within the realm of Cyber-Physical Systems (CPS). Take scenarios like autonomous vehicles and surveillance systems, for instance, where denoising plays a pivotal role in significantly improving the accuracy of object detection and recognition. However, the adoption of image denoising tasks in CPS is hindered by the fragility, robustness, and interpretability issues associated with neural networks. To address these challenges, this study introduces an innovative and interpretable approach to image denoising. We propose an image denoising model that combines dictionary learning with a deep neural network. This hybrid approach leverages decoupling and sparse convolution techniques, strategically designed to mitigate model fragility and reinforce model robustness. Furthermore, our model is geared towards untangling and reducing redundancy across different image components. The architecture of the network is crafted as a model-data-driven neural network, facilitating the simultaneous learning of various image components and deploying fusion mechanisms to mitigate perturbations and noise. Finally, we provide a theoretical framework to explain our method and substantiate its effectiveness through rigorous experimentation and validation.

关键词： Dictionary learning Sparse Coding Image Denoising Decomposition Decoupling

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DiffGSL: A Graph Structure learning Diffusion Model for Dynamic Spatio-Temporal Forecasting

DiffGSL: A Graph Structure Learning Diffusion Model for Dyna...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Jung, Chanyoung Jang, Yun Sejong University Seoul Korea Republic of

ISBN: (纸本)9798350362480

Researchers have been actively engaged in recent studies utilizing the denoising diffusion probabilistic model (DDPM), a generative model that shows stable convergence during training, unlike other generative models, and represents data distributions that approximate real-world data. Due to these advantages, DDPM is widely employed for image, video, and audio generation. Recent DDPM research extends beyond image, video, and audio to various datasets and applications. The majority of real-world scenarios involve spatiotemporal data. Consequently, researchers propose prediction models using spatiotemporal data across domains such as traffic forecasting, weather prediction, and disease tracking. While most spatiotemporal data prediction models focus on traffic data, weather and epidemic data present higher uncertainty due to regional variations, seasonal changes, fluctuations in infection rates, and uncertainties in reported cases. To address these challenges of spatiotemporal data, we propose a DDPM-based model. In contrast to previous DDPM studies, we propose a denoising network combining Graph Structure learning (GSL) to utilize the dynamic nature of spatiotemporal data. We assess performance using three real-world datasets encompassing traffic, weather, and epidemics and conduct an ablation study comparing GSL methods. © 2024 IEEE.

关键词： Prediction models

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Meta-learning Algorithms for Few-Shot Classification in Imbalanced datasets 4

Meta-Learning Algorithms for Few-Shot Classification in Imba...

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4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Raman Fezaa, Laith H. Alkhayyat, Ahmed Pareek, Bhagwati Lal Kumar, Jatinder Jadhav, Abhilasha School of Mechanical Engineering Rayat Bahra University Mohali India Al-Zahrawi University College Department of Optical Techniques Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq Sangam University Rajasthan Bhilwara India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science Engineering Jhanjeri Punjab Mohali140307 India Nims School of Computing Science and Artificial Intelligence Nims University Rajasthan Jaipur India

ISBN: (纸本)9798350387490

This work aims to examine meta-learning on few-shot learning schemes, specifically on the assessment of how well current approaches manage imbalance in the examined datasets. Meta-learning to improve model performance with a small amount of data by learning from related tasks is tested against benchmarks such as MAML, ProtoNet and RelationNet. An important insight of our research is that these methods are still facing important issues, such as scalability or the problem of domain generalization, as well as the major issue of the extreme imbalance of the data distribution. To overcome these problems, we suggest a new solution, which employs elaborate methods and is characterized by increased correspond and F1 coefficient, as well as the degree of balanced accuracy at different degrees of unbalanced data. However, the proposed study also points out the main challenges for future research including new and efficient algorithms for high-dimensional and real-time scenario, better methods for handling massive shift, as well as the possibility to increase the interpretability of models. The research also highlights the possibilities of applying meta-learning in the few-shot classification, but at the same time also highlight the imperfections and the research needs to be continued in order to further improve the existing gaps and to meet the practical requirements. This study adds to the overall knowledge base concerning meta learning approaches and provides an initial frameworkfor work to be conducted in the future in an effort to produce methods that are considerably more portable, generalizable and also more comprehensible in nature across the broader field of study. © 2024 IEEE.

关键词： Contrastive learning

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MCDPS: Enhancing Clinical Decision Support for Multiple Chronic Disease Prediction Systems Using Ensemble machine learning Approaches 4th

MCDPS: Enhancing Clinical Decision Support for Multiple Chro...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Kumar, Kanak Verma, Anshul Verma, Pradeepika Banaras Hindu Univ Dept Comp Sci Varanasi 221005 Uttar Pradesh India Indian Inst Technol Technol Innovat Hub Patna 801103 Bihar India

ISBN: (纸本)9783031821554;9783031821561

People today suffer from various chronic diseases due to lifestyle choices and environmental conditions. Early prediction of these diseases is crucial to prevent them from worsening. However, it is often challenging for physicians to accurately diagnose these issues independently. A computational model based on big data analytics has numerous applications in the medical field. Advancements in machine learning (ML) and information technology have enabled more accurate recognition of diseases, health emergencies, and conditions. The advent of predictive algorithms has transformed disease diagnosis in the medical field. Previously, patient data records were meticulously examined to predict diseases and develop advanced models for trend analysis. However, with the rise of data analytics-driven intelligence, the evaluation of disease symptoms has become more accurate and efficient. These technologies enable the creation of predictive models capable of analyzing vast amounts of patient data to identify hidden patterns, aiding in early disease detection and diagnosis. This study explores the development of an ensemble ML-based multiple chronic disease prediction system (MCDPS) focused on three major diseases: chronic kidney disease (CKD), liver disease, and heart disease. The system utilizes ensemble ML models, including Decision Tree (DT), Gradient Boosting Classifier (GBC), Extreme Gradient Boosting (XGBoost), and Extra-Trees classifiers (ETC), to predict diseases. Its user-friendly and straightforward design makes it accessible to beginners. The system's performance in predicting the three chronic diseases has been evaluated and compared with other traditional models. The results show that the DT algorithm achieved an accuracy of 99%, 85%, and 83% for predicting CKD, heart disease, and liver disease, respectively.

关键词： Multiple Chronic Disease Prediction System machine learning Decision Tree Gradient Boosting Classifier Extreme Gradient Boosting Extra-Trees classifiers

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Towards an Efficient DDoS Attack Detection in SDN: An Approach with CNN-GRU Fusion 4

Towards an Efficient DDoS Attack Detection in SDN: An Approa...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Goud, Konda Srikar Rao, Giduturi Srinivasa Gitam University Gitam School of Technology Department of Computer Science and Engineering AP Visakhapatnam India Bvrit Hyderabad College of Engineering for Women Telangana Hyderabad India

ISBN: (纸本)9798350343670

The emergence of Distributed Denial of Service (DDoS) attacks presents a significant risk to Software-Defined Networking (SDN) services. This study focuses on developing an effective method for detecting DDoS attacks in SDN by leveraging Deep learning (DL) techniques, precisely combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). This research proposes a novel approach that utilizes CNN-GRU-based DL models to detect DDoS attacks in SDN accurately. This approach learns intricate patterns and temporal dependencies associated with DDoS attacks by analyzing network traffic data and detecting such threats. In order to assess the effectiveness of our model, we conduct extensive experiments using diverse datasets comprising both normal network traffic and various types of DDoS attacks. We present a comparative analysis against traditional ML algorithms and existing methodologies. To assess the effectiveness of our approach, we used various performance metrics. The experimental results prove that the CNN-GRU-based DL model outperforms other techniques, exhibiting superior accuracy and overall performance in detecting DDoS attacks in SDN. © 2024 IEEE.

关键词： Convolutional Neural Networks DDoS attacks Deep learning Gated Recurrent Units SDN

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Edge Energy Orchestration 20th

Edge Energy Orchestration

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20th international conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Kumar, Vijay Valizadeh, Nima Petri, Ioan Rana, Omer Nag, Ambarish Zisman, Sagi Tripp, Charles Paul, Shuva School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Engineering Cardiff University Cardiff United Kingdom Colorado United States Colorado United States

ISBN: (纸本)9783031812255

Edge computing devices have increased in number and capability over recent years. The ability to process data and execute machine learning in proximity to data generation and collection sources provides several advantages over using cloud- based data centers. We describe an orchestration mechanism that enables edge devices to make more effective use of energy resources in their proximity – a technique we refer to as "edge energy orchestration". A software "orchestrator" can take account of renewable generation to alter how task execution on edge devices is carried out. An application scenario is used to illustrate the use of the orchestrator in practice, followed by a discussion about how this approach can be generalized to a broader set of applications © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clean energy

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Knowledge-Enhanced Contextual Auction Design Based on Deep Neural Networks

Knowledge-Enhanced Contextual Auction Design Based on Deep N...

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2024 IEEE international conference on Cognitive computing and Complex data

作者： Liang, Mingxuan Guo, Zhibo Zhu, Junwu Zhang, Yuanyuan Li, Xueqing Yangzhou Univ Dept Informat Engn Yangzhou Jiangsu Peoples R China

ISBN: (纸本)9798350352900;9798350352894

The optimal auction represents a fundamental challenge in auction design, primarily focused on maximizing the seller's revenue. Recently, context-aware auction design has gained traction as it enhances the model's understanding of the auction environment by incorporating contextual information about buyers and items. However, limited contextual information can hinder the model's ability to capture potential correlations. Therefore, we propose a Knowledge-Enhanced Contextual Auction Design Model (KECA) based on neural networks. KECA aims to improve the comprehensive understanding of the auction environment by integrating external knowledge. Specifically, we first construct a structured knowledge graph of triples using Wikipedia. This knowledge graph is utilized to enhance the contextual feature representation of buyers and items, thereby providing a more comprehensive description of the information in the auction environment. Additionally, we employ the K-means clustering algorithm to enhance feature fusion between auction information and the knowledge graph within the locally sensitive hashing self-attention mechanism, thereby improving the model's feature representation capabilities. Finally, extensive experimental results indicate that KECA outperforms current mainstream auction models under various settings, verifying the effectiveness of this approach.

关键词： Auction Design Deep learning Optimal Auction Knowledge Graph

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FedZipper: A Layer-wise Quantization Compression Framework for Federated learning with Statistical Heterogeneity 20th

FedZipper: A Layer-wise Quantization Compression Framework f...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Du, Haizhou Peng, Yan Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9789819628636

Quantization is a common compression scheme for reducing traffic in federated learning that converts data from floating-point numbers to a low-precision representation. Existing quantization approaches typically apply fixed degrees of model parameter quantization on non-IID data at a coarse-grained level. It hurts the whole accuracy of the training model and fails to improve the efficiency of the federated learning process. In the paper, we propose a layer-wise quantization compression framework for federated learning with statistical heterogeneity, namely FedZipper. FedZipper can identify and quantify the critical layers of the learning model on each client. Moreover, FedZipper adopts an adaptive mechanism to aggregate different transmitted layers from different clients. Our extensive experiments show up to 38.8% improvement in the communication data volume for the whole training process while less decline in model accuracy. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning

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