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

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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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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...

引用

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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Advanced Deep learning Approaches for Real-Time Anomaly Detection in IoT Environments 7

Advanced Deep Learning Approaches for Real-Time Anomaly Dete...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Goyal, Himanshu Rai Husain, Saif O. Dixit, Krishna Kant Boob, Nandini Shirish Reddy, B. Ravali Kumar, Jaysheel Sharma, Smita Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Amity University Amity School of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

The proliferation of Internet of Things (IoT) systems has led to the generation of vast amounts of data, increasing the need for effective anomaly detection mechanisms to ensure system reliability and security. Traditional methods often fall short due to the high dimensionality and dynamic nature of IoT data. This paper presents a deep learning-based approach for real-time anomaly detection in IoT systems, leveraging the power of neural networks to identify patterns and anomalies within complex datasets. By employing deep learning algorithms such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, this framework can efficiently detect abnormal behaviors in real-time, offering a significant improvement over conventional methods. The proposed system is trained on labeled datasets collected from IoT devices, allowing it to distinguish between normal and anomalous activity. Furthermore, the model adapts to new data, enhancing its detection capabilities over time. Experimental results demonstrate that this approach achieves high accuracy and low false-positive rates, making it suitable for deployment in real-world IoT environments. The framework's scalability and adaptability highlight its potential for enhancing the security and reliability of IoT networks, particularly in critical applications such as smart cities, healthcare, and industrial IoT. © 2024 IEEE.

关键词： Convolutional neural networks

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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...

引用

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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Unsupervised Nonlinear Deformable Registration Network for 4D CT Lung Imaging 25

Unsupervised Nonlinear Deformable Registration Network for 4...

引用

25th international conference on Digital Image computing: Techniques and Applications, DICTA 2024

作者： Iqbal, M Zafar Ulhaq, Anwaar Razzak, Imran School of Engineering and Technology Central Queensland University Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Deformable image registration is crucial in various image-guided radiation therapy applications, particularly in registering computed tomography (CT) lung image modalities during extreme inspiration and expiration phases. The primary challenge is effectively addressing nonlinear deformation between these phases. Advancements in deep learning techniques provide efficient alternatives to traditional approaches, promising enhanced accuracy and computational efficiency. This study presents an unsupervised approach called the Image Patch Decomposition Registration Network (IPDRN) designed for 4D CT lung image registration, which operates independently of ground truth data. The IPDRN effectively learns multi-resolution and multi-scaling features, facilitating more accurate deformation vector field (DVF) computation. The integration of regularization, image dissimilarity and Jacobian determinant loss functions enhances its ability to capture small and complex deformations in image subjects. The effectiveness of the proposed registration method is assessed by comparing the target registration error (TRE) with those produced by conventional algorithms and state-of-the-art (SOTA) unsupervised registration methods. © 2024 IEEE.

关键词： Deep learning

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A Hybrid Deep learning Approach for Bone Defect Detection using DCGAN and CNN 1

A Hybrid Deep Learning Approach for Bone Defect Detection us...

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1st international conference on Technological Innovations and Advance computing, TIACOMP 2024

作者： Pundir, Brajesh Singh Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Cse Punjab India University of Kufa Faculty of Computer Science and Mathematics Dept of Computer Science Najaf Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350392111

Bone fractures, caused by accidental traumas and medical disorders, can harm the human skeletal system, which provides structural support and protects vital organs. Leveraging Synthetic Image Generation via Generative Adversarial Networks (GANs) greatly improves bone defect accuracy in diagnosis by providing diverse and vast data sets for training. This novel approach results in stronger as well as precise diagnostic models, which enable early and reliable identification of diverse bone diseases. Here, CNNs are utilized in bone fracture images particularly in identifying trends and objects in complex visual inputs of bone fractures. The classification model performed admirably, with an overall accuracy of 98%, as well as high precision, recall, and F1-scores of around 98% across both categories, indicating its ability to distinguish among both categories with impressive reliability and consistency. The next phase of this research is to broaden GAN capabilities to address a broader range of medical imaging challenges, with the promise of not only enhancing diagnostic precision across multiple conditions but also personalising methods of treatment, thus creating new standards in healthcare creativity and patient care. © 2024 IEEE.

关键词： Patient treatment

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Span-Based Chinese Few-Shot NER with Contrastive and Prompt learning 13th

Span-Based Chinese Few-Shot NER with Contrastive and Prompt ...

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13th international conference on Natural Language Processing and Chinese computing

作者： Ye, Feiyang Lai, Peichao Yang, Sanhe Zhang, Zhengfeng Wang, Yilei Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

For Chinese Named Entity Recognition (NER) tasks, achieving better performance with fewer training samples remains a challenge. Previous works primarily focus on enhancing model performance in NER by incorporating additional knowledge to construct entity features. These approaches neglect the semantic information of entity labels and the information of entity boundaries. Moreover, conventional methods typically treat NER as a sequence labeling task, which makes them inadequate for addressing the issue of nested entities. We propose a new span-based approach by using contrastive learning and prompt learning to address these problems. By pulling similar entities closer together, pushing dissimilar entities further apart, and leveraging entity label information, we improve model performance in few-shot scenarios effectively. Experimental results demonstrate that our method achieves significant performance improvements on a sampled Chinese nested medical dataset and several other flattened datasets, providing a new insight into addressing challenges in few-shot NER tasks.

关键词： Named Entity Recognition Contrastive learning Few-shot learning

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A Secure Certificateless Multi-signature Scheme for Wireless Sensor Networks 24

A Secure Certificateless Multi-signature Scheme for Wireless...

引用

8th international conference on machine learning and Soft computing, ICMLSC 2024

作者： Lin, Han-Yu Tsai, Tung-Tso Hsu, Jing-Yu Department of Computer Science and Engineering National Taiwan Ocean University Taiwan

ISBN: (纸本)9798400716546

In the application of wireless sensor networks (WSNs), lots of deployed sensor nodes will forward authenticated message to a base station for verification. The technique of data aggregation is thus become important, since it can gain more bandwidth savings and the base station can also verify the authenticity of aggregated data within one logical step. To fulfill such an application requirement, the so-called multi-signature scheme is applicable, since it allows multiple signers cooperatively to sign a message so that any verifier can authenticate the generated multi-signature with the aggregated public keys of all participated signers within one logical step. In a traditional multi-signature scheme, a public key has to be verified before using it, so as to prevent from the public substitution attack. A public key certificate issued by a trusted certificate authority is a commonly utilized solution. However, it will increase extra costs for transmitting and verifying the certificate. To avoid the troublesome certificate management, a certificateless public key system is a better alternative. In such a cryptosystem, each entity's complete private key is divided into two parts. The first part is a partial private key issued by a key generation center (KGC) while the second part is a secret integer chosen by the entity. In this paper, the authors propose a certificateless multi-signature scheme to fulfill the requirement of data aggregation in WSNs. The security of existential unforgeability for the proposed mechanism is formally proved on the basis of the hardness of Computational Diffie-Hellman (CDH) problem. Furthermore, the efficiency comparisons with related works are also evaluated. © 2024 ACM.

关键词： Base stations

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Visual malware classification and detection method based on convolutional neural network 9

Visual malware classification and detection method based on ...

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9th IEEE international conference on data science in Cyberspace, DSC 2024

作者： Huang, Hua Sheng, Hua Han, Xingwang Zhang, Wenbin Zhao, Zilin State Grid Shandong Electric Power Company Information and Telecommunications Company Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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