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检索条件"任意字段=IEEE Conference on Local Computer Networks"

共 18625 条记录，以下是491-500 订阅

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Hybrid CNN-GCN Model for Tumor Classification: Integrating Spatial Relationships in Medical Imaging

Hybrid CNN-GCN Model for Tumor Classification: Integrating S...

引用

2024 International conference on Decision Aid Sciences and Applications, DASA 2024

作者： Voddi, Swathi Sirisha, Uddagiri Praveen, S. Phani Sai Pandraju, Thandava Krishna Al-Dmour, Nidal A. Islam, Shayla Koneru Lakshmaiah Education Foundation Department of CSE Vaddeswaram India Prasad V. Potluri Siddhartha Institute of Technology Department of CSE Vijayawada India Dhanekula Institute of Engineering and Technology Department of CSE Vijayawada India College of Engineering Department of Computer Engineering Mutah University Jordan Institute of Computer Sciene and Digtial Innovation UCSI University Malaysia Kuala lumpur56000 Malaysia

ISBN: (纸本)9798350369106

An effective method of classifying medical images, particularly for tumor diagnosis, is proposed in this paper using a hybrid system combining convolutional neural networks (CNNs) and graph convolutional networks (GCNs). A high-dimensional representation of visual patterns and textures is extracted from input images using ResNet-18. By connecting pixels to their top-k nearest neighbours, these features are transformed into a graph structure that models spatial relationships among the pixels. A GCN layer aggregates data from neighbouring nodes to learn both local and global dependencies on a graph. To produce robust, image-level classifications, the model averages node-level predictions. It is designed to capture complex spatial relationships and fine-grained spatial details, which makes it an excellent tool for challenging medical tasks. © 2024 ieee.

关键词： Convolutional neural networks

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Multi-Aperture Transformers for 3D (MAT3D) Segmentation of Clinical and Microscopic Images

Multi-Aperture Transformers for 3D (MAT3D) Segmentation of C...

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2025 ieee/CVF Winter conference on Applications of computer Vision, WACV 2025

作者： Sohaib, Muhammad Shabani, Siyavash Mohammed, Sahar A. Winkelmaier, Garrett Parvin, Bahram University of Nevada Department of Electrical and Biomedical Engineering Reno United States Pennington Cancer Institute United States

ISBN: (纸本)9798331510831

3D segmentation of biological structures is critical in biomedical imaging, offering significant insights into structures and functions. This paper introduces a novel segmentation of biological images that couples Multi-Aperture representation with Transformers for 3D (MAT3D) segmentation. Our method integrates the global context-awareness of Transformer networks with the local feature extraction capabilities of Convolutional Neural networks (CNNs), providing a comprehensive solution for accurately delineating complex biological structures. First, we evaluated the performance of the proposed technique on two public clinical datasets of ACDC and Synapse multi-organ segmentation, rendering superior Dice scores of 93.34± 0.05 and 89.73± 0.04, respectively, with fewer parameters compared to the published literature. Next, we assessed the performance of our technique on an organoid dataset comprising four breast cancer subtypes. The proposed method achieved a Dice 95.12± 0.02 and a PQ score of 97.01± 0.01, respectively. MAT3D also significantly reduces the parameters to 40 million. The code is available on https://***/sohaibcs1/MAT3D. © 2025 ieee.

关键词： Convolutional neural networks

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Graph Neural Network Algorithm Based on Graph Convolution and Attention Mechanism 5

Graph Neural Network Algorithm Based on Graph Convolution an...

引用

5th ieee International conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Xu, Feifei Zhang, Yumeng Li, Yifei Qufu Normal University Rizhao China

ISBN: (纸本)9798331533694

With the wide application of graph neural network (GNN) in many fields, how to extract and aggregate node features effectively has become a hot research issue. In this paper, we propose a graph neural network algorithm that leverages graph convolution and the attention mechanism to enhance the expressive power and aggregation efficacy of node features within a single subgraph. The model's core framework comprises two primary steps: Firstly, node features are extracted from subgraphs utilizing the graph convolution algorithm. In particular, the graph convolution operation updates the feature representation of each node by disseminating information from its adjacent nodes. This process can capture local structural information and effectively integrate the neighborhood features of nodes, thus enhancing the semantic representation of features. Secondly, using the extracted node features, the model incorporates an attention mechanism to aggregate the features in a weighted manner. By evaluating the similarity between each node and its neighboring nodes, the attention mechanism is able to dynamically assign weights to various nodes, thereby emphasizing the significance of key nodes during the aggregation process. This method overcomes the limitation of treating all neighbor nodes equally in the traditional aggregation method, and can more accurately reflect the relevance and importance of the nodes in the subgraph. The experimental outcomes show that the proposed algorithm outperforms existing techniques in terms of performance across various graph datasets. © 2025 ieee.

关键词： Graph neural networks

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A Community Detection Algorithm for Multi-View Attributed Network 16

A Community Detection Algorithm for Multi-View Attributed Ne...

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16th International conference on computer and Automation Engineering, ICCAE 2024

作者： Chen, Dongming He, Yuxing Xie, Fei Nie, Mingshuo Wang, Dongqi Ren, Tao Software College Northeastern University Shenyang China

ISBN: (纸本)9798350370058

Multi-view attributed networks community detection is much more challenging than common simple or attributed networks community detection, because each view may contain noisy edges and connections and also some key information are often shared among views. We propose a community detection method called the MvCGCD algorithm for multi-view attributed network in this paper. The proposed algorithm firstly removes the unwanted high frequency noise by using a graph filter, then it employs a graph convolution network (GCN) to aggregate the local neighborhood information of each node to obtain the new representation of the node. Experimental results on multi real work datasets show that MvCGCD algorithm out performed the baseline algorithms, the best performance case was up to 25.39%. © 2024 ieee.

关键词： Complex networks

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Leveraging Quantum Communication with 6G Network: Optimize the Model for Secured Communications 1

Leveraging Quantum Communication with 6G Network: Optimize t...

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1st International conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar Gautam, Anupam Kumar Rajendran, Naveen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka India

ISBN: (纸本)9798331518578

A broad-ranging consciousness-raising movement of mathematical ideas is dawning accompanying the onset of 6G science, that promises ubiquitous and flowing relations. Many visualize the inclusion of quantum computing (QC) into specific networks as a potential responsible for the complex computational and safety needs concerning this revolution. In light of 6G science, this paper delves further into the likely functions and results of QC. Totally classification of some (AP15) fault-finding QC uses in 6G science into four main classifications established in the literature review. Speaking accompanying experts engaged rooted these results even further;they uncovered best practices guide individual of the emphasize fault-finding applications. Researchers in addition to manufacturing experts alike grant permission to benefit from the research's judgments, which determine a firm basis for including the detracting uses of QC within 6G Wi-Fi electronics and profitable arrangements for implementing it. © 2025 ieee.

关键词： Wireless local area networks (WLAN)

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Topo-Loss: A Novel Loss Function for Learning Multiple Manifold Structures in Image Classification with Deep Neural networks 5

Topo-Loss: A Novel Loss Function for Learning Multiple Manif...

引用

5th International conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Ran, Likai Cai, Chao Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350373820

abstract-The real data distribution of a specific category tends to approach a low-dimensional manifold in the Riemannian space, and the manifolds of different categories do not intersect in high-dimensional space. However, existing loss functions used for image classification ignore the inherent manifold structure between samples, resulting in limited network performance. In this paper, we reveal that maintaining the manifold structure between samples while extracting features can significantly improve the efficiency of neural networks in image classification tasks and propose a novel loss function called Topo-Loss. Topo-Loss is calculated by a dual-branch structure network. One of the branches is used to calculate the cross-entropy loss, and the other branch is used to get the cosine loss between the local geodesic distance of samples in the feature space and it in the original space. The optimization goal of the network is to minimize the weighted sum loss of the two branches, thus allows the neural network to maintain the manifold structure between samples during the feature extraction. The visualization experiments show that the DNN gains the ability to learn multiple manifolds with Topo-Loss simultaneously, significantly improving the network's performance in image classification. Meanwhile, our work outperforms other similar works. © 2024 ieee.

关键词： Image classification

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The Application of Deep Learning Techniques in computer Security for Big Data Anomaly Detection 2

The Application of Deep Learning Techniques in Computer Secu...

引用

2nd ieee International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Duan, Zhijuan Wuhan University of Engineering Science Hubei Wuhan China

ISBN: (纸本)9798331504960

Today, with the rapid development of the Internet, the requirements for network security are increasing day by day. This article explores the application of deep learning technology, especially convolutional neural networks (CNN), in anomaly detection in big data environments. The study designed a CNN-based anomaly detection framework to simulate the way the human brain processes visual information, achieving automatic extraction of complex features in data and recognition of abnormal patterns. Research methods include building multi-layer CNN models, data preprocessing, feature extraction and anomaly scoring, as well as comparative analysis of experimental designs. Experimental results show that CNN performs well in different network attack scenarios, with an average recall rate of 94.9%, a precision rate of 92.6%, and an average accuracy rate of 93.75%, which is significantly better than the local anomaly factor (LOF) and principal component analysis (PCA).) and kernel density estimation (KDE) and other traditional methods. Additionally, CNN has lower response times when detecting advanced persistent threats, ransomware attacks, and distributed denial-of-service attacks. As an emerging abnormal behavior identification method, CNN can quickly and accurately find targets for users, which is of great significance to information security in today's Internet environment. © 2024 ieee.

关键词： Convolutional neural networks

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Siamese Multiple Attention Temporal Convolution networks for Human Mobility Signature Identification 27

Siamese Multiple Attention Temporal Convolution Networks for...

引用

27th ieee International conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Zhipeng Jiang, Yuchen Zhang, Shiyao Wei, Xuetao Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798331505929

The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes. © 2024 ieee.

关键词： Convolution

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MobileP2VT: Parallel Hybrid Structure makes Lightweight Network Stronger for Diabetic Retinopathy Classification

MobileP2VT: Parallel Hybrid Structure makes Lightweight Netw...

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2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ji, HongXu Wu, Jie Xi, Heran Zhu, Jinghua Heilongjiang University School of Computer Science and Technology Harbin150000 China

ISBN: (纸本)9798350386226

The emergence of lightweight models provides a new solution for the classification of Diabetic Retinopathy (DR). These lightweight models have a fewer parameters and low computational complexity. However, existing lightweight models in Diabetic Retinopathy (DR) still face challenges: Convolutional Neural networks (CNNs) are limited by their receptive fields and lack modeling capabilities over long distances. Although the models based on Transformer can capture global information, the usually have high computational complexity. Recent lightweight models that try to combine the strengths of CNN and Transformer tend to have a simple serial architecture that fails to take full advantage of both. To address these challenges, we propose a new lightweight network, MobileP2VT, for the classification of diabetic retinopathy. The model uses lightweight convolution and Transformer parallel processing to capture local details and global information of the image. In addition, we have designed a new attention mechanism, Sparse Token Attention (SPTA), which reduces the amount of attention computation in Transformer. Experimental results on Eyepacs and APTOS 2019 datasets demonstrate the high efficiency of MobileP2VT. © 2024 ieee.

关键词： Convolutional neural networks

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Pyramid Pooling-based local Profiles for Graph Classification

Pyramid Pooling-based Local Profiles for Graph Classificatio...

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2023 ieee International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Chengpei Lou, Yang Li, Junli College of Computer Science Sichuan Normal University Chengdu610066 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Graduate School of Information Science and Technology Osaka University Osaka565-0871 Japan

ISBN: (纸本)9798350337020

Many natural and engineering systems can be modeled and represented in the forms of graph data, and then studied using graph theory and network analysis tools. Graph representation learning aims at generating lower-dimensional representations from higher-dimensional graph data, which is a crucial step that facilitates the follow-up tasks, such as node and graph classifications. In this paper, we present a simple but effective graph representation learning method, namely the pyramid pooling-based local profile (PPLP), which enables local nodal profiles to be transformed into a graph representation, with multi-scale features extracted. PPLP can be either embedded into a graph neural network as the readout layer, or perform independently as a graph embedding algorithm. The resultant representations of PPLP are for graph-level tasks. PPLP is experimentally tested by performing graph classification tasks on ten representative datasets, either as the readout layer of different graph neural networks, or as an independent graph embedding algorithm. Experimental results demonstrate that: 1) when embedded into graph neural networks, PPLP outperforms the widely-used global pooling-based readout methods;2) as an independent graph embedding algorithm, PPLP performs fairly good, especially on the social network datasets. The investigation confirms PPLP as a simple but promising method for graph-level tasks. © 2023 ieee.

关键词： graph classification graph neural network Graph representation learning pyramid pooling

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