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

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Explainable RNN Classification of Right Ventricular Dysfunction from Echocardiogram Reports

Explainable RNN Classification of Right Ventricular Dysfunct...

引用

2024 ieee International conference on Big Data, BigData 2024

作者： Rajaratanam, Arun Corizzo, Roberto Boukouvalas, Zois Mid-Atlantic Permanente Group AlexandriaVA United States American University Department of Computer Science WashingtonDC United States American University Dept. of Math and Statistics WashingtonDC United States

ISBN: (纸本)9798350362480

Accurately identifying patients with Right Ventricular Dysfunction (RVD) is critical for timely diagnosis and treatment, yet it remains a significant challenge in clinical practice due to the complexity and variability of echocardiography text reports. In this study, we address this crucial problem by evaluating the effectiveness of recurrent neural networks (RNNs) in classifying patients with and without RVD from echocardiography text reports. We demonstrate that our model exhibits strong performance, with high recall and precision in identifying RVD, underscoring its potential utility in clinical settings. Furthermore, we enhance the interpretability of the framework by leveraging the local Interpretable Model-Agnostic Explanations (LIME) framework. This approach allows us to extract and validate meaningful semantic information related to RVD, providing deeper insights into the model's local predictive capabilities and improving transparency in decision-making. © 2024 ieee.

关键词： Patient treatment

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Research on Fault Prediction of computer Network Nodes Based on Log information 16

Research on Fault Prediction of Computer Network Nodes Based...

引用

16th International conference on Communication Software and networks, ICCSN 2024

作者： Wang, Yuxi Ye, Qingwei Zhou, Peng Li, Bing Wang, Xiaodong Zhang, Lianjun Ningbo University School of Information Science and Engineering Ningbo China

ISBN: (纸本)9798331506322

Aiming at the impact of node faults on normal business operation in computer networks, a log information-driven fault prediction method is proposed. By constructing an efficient deep learning model and introducing a correction mechanism, node faults in computer networks are predicted and diagnosed to support network operation and maintenance requirements. Firstly, the log information generated by each node in the computer network is collected, the state vector of each node and the state matrix of all nodes are obtained, and then the data set is supplemented by the state filling principle. Finally, the fault prediction problem is transformed into a time series prediction problem. The performance was evaluated in GAIA, a public smallscale operation and maintenance data set. The experimental results show that compared with other algorithms, the proposed model has a good prediction effect in local network scenarios, and the prediction effectiveness has been verified, which provides a certain reference value for computer network fault prediction research. © 2024 ieee.

关键词： Prediction models

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A Novel Ant Colony and Sparrow Search Optimization-Based Routing Algorithm for LEO Satellite networks 4

A Novel Ant Colony and Sparrow Search Optimization-Based Rou...

引用

4th ieee International conference on Data Science and computer Application, ICDSCA 2024

作者： Li, Yuanfeng Zhang, Qi Wang, Fu Tian, Feng Feng, Weiying Tian, Qinghua Wang, Yongjun Yang, Leijing Chai, Furong Meng, Sun Li, Xiaohu Beijing Key Laboratory of Space-ground Interconnection and Convergence Beijing100876 China School of Materials Science and Engineering Beihang University Beijing100191 China Beijing100876 China

ISBN: (纸本)9798350368239

To improve the convergence capability of ant colony routing in LEO satellite networks, a novel hybrid ant colony-sparrow search optimization routing algorithm is proposed. The sparrow algorithm is used to identify local optimal solutions in the ACO routing process and guide the algorithm to re-converge. Simulation results show that, compared to the traditional ACO algorithm, the proposed algorithm significantly improves communication delay and reduces communication costs. ©2024 ieee.

关键词： Ant colony optimization

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A Data Retrieval Method Based on AGCN-WGAN 4

A Data Retrieval Method Based on AGCN-WGAN

引用

4th ieee International conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Sun, Geng Peng, Guotao Tian, Xiaolei Li, Lu Zhao, Yuqi Wang, Yue State Grid Liaoning Electric Power Co. Ltd. Fuxin Power Supply Company Fuxin China State Grid Liaoning Electric Power Co. Ltd Fuxin China Northeastern Electric Power University Jilin China

ISBN: (纸本)9798350359589

Traditional knowledge graph retrieval techniques ignore node relationship weights, making it difficult to achieve targeted retrieval. Therefore, a nonlinear model AGCN-WGAN is proposed to solve retrieval tasks in relational networks, fully utilizing the advantages of Graph Convolutional networks (GCN) and Generative Adversarial networks (GAN). Firstly, AGCN is used to capture the local topological features of a single node;In addition, the use of GAN enhances the ability of AGCN models to generate reasonable weight distribution maps, effectively extracting correlations between nodes, thereby improving the performance of the model in handling large-scale data retrieval tasks. In order to verify the effectiveness of the method, the dispatching operation data in a real business scenario of a city power grid is used for experiments. The experimental results show that the proposed data retrieval method has significantly improved accuracy compared to existing methods. © 2024 ieee.

关键词： Generative Adversarial networks Graph Convolutional networks Knowledge graph retrieval Node correlation

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Improving Proximity Classification for Contact Tracing using a Multi-channel Approach 47

Improving Proximity Classification for Contact Tracing using...

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47th ieee conference on local computer networks (LCN)

作者： Lanfer, Eric Haenel, Thomas Van Rijswijk-Deij, Roland Aschenbruck, Nils Osnabruck Univ Inst Comp Sci Osnabruck Germany Univ Twente Design & Anal Commun Syst Grp Enschede Netherlands

ISBN: (纸本)9781665480017

Due to the COVID-19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use Bluetooth Low Energy (BLE) signal strength data to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does hardly deliver accurate results. We present a multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched ieee 802.11 (2.4 & 5 GHz) and BLE signal strength data, measured in four different environments. We utilize these data to train machine learning models. The evaluation showed significant improvements in the distance classification and consequently also the contact tracing accuracy. However, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.

关键词： Contact Tracing Proximity Classification Bluetooth Low Energy COVID-19 ieee 802.11

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Exploring Sentiment Analysis: A Hybrid Approach using Deep Learning and Encoder 1

Exploring Sentiment Analysis: A Hybrid Approach using Deep L...

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1st ieee International conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Mishra, Supriya Ranjan, Roop Singhania University Computer Science and Engineering Rajasthan Jhunjhunu333515 India Greater Noida Institute of Technology Engineering Institute Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350356816

The rapid expansion of internet data underscores the increasing significance of timely analysis and monitoring of public sentiments online. In this context, analyzing sentiments related to public opinion events becomes paramount. Traditional word2vec models often fall short in fully capturing the depth of information within words. To address this, utilizing the BERT model for article feature extraction is proposed. This is coupled with a deep convolutional neural network to capture local article information. Subsequently, a fully connected network is employed to classify the articles, enabling effective sentiment analysis. Experimental findings from public datasets showcase the efficacy of sentiment analysis algorithms leveraging BERT in tandem with convolutional neural networks." © 2024 ieee.

关键词： Convolutional neural networks

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Enhancing Road Safety with Neural Network Segmentation Models: A Deep Dive into Crack Detection 4

Enhancing Road Safety with Neural Network Segmentation Model...

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4th ieee Interdisciplinary conference on Electrics and computer (INTCEC)

作者： Alharbi, Shuaa S. Qassim Univ Coll Comp Dept Informat Technol Buraydah 52571 Saudi Arabia

ISBN: (纸本)9798350349467;9798350349450

Road crack segmentation plays a pivotal role in infrastructure maintenance, ensuring the safety and longevity of road networks. This study presents a meticulous investigation into the efficacy of various U-Net variants and contemporary models for road crack segmentation using a well-annotated dataset. In this study, we evaluated the performance of ResU-Net++, U-Net, U-Net++, U-Net3+, TransResU-Net, DeepLabV3+, Polyp-PVT, and PraNet. Notably, Poly-PVT exhibited remarkable performance, achieving a Jaccard index of 0.5648 and an F1-score of 0.7063, signifying superior crack segmentation capabilities. Furthermore, TransResU-Net's comparatively lower metrics indicate potential feature overcomplication for this dataset. The findings suggest that Poly-PVT with its full-scale multi-level feature fusion excels in capturing both local nuances and global context, solidifying its position as an effective model for road crack segmentation. This comprehensive evaluation contributes valuable insights to the field, aiding researchers and practitioners in selecting the most suitable model for this critical infrastructure maintenance task.

关键词： Artificial Intelligent Convolutional Neural Network Deep Learning Deep Neural networks Image Segmentation Machine Learning Road Crack

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CRULoc: A protein subcellular localization prediction model based on BiGRU and CNN 7

CRULoc: A protein subcellular localization prediction model ...

引用

7th International conference on computer Information Science and Application Technology, CISAT 2024

作者： Lu, Yufei Henan University School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9798350375107

The prediction of subcellular localization of eukaryotic proteins is a pivotal area in bioinformatics research, crucial for elucidating protein functions and mechanisms. This study proposes a novel CRULoc model, which integrates Bidirectional Gated Recurrent Units (BiGRU), Convolutional Neural networks (CNN), and three pooling strategies (soft pooling, max pooling, and average pooling). The model uses embedding features generated by the protein language model ProtT5-XL-U50 as input. Within the CRULoc model, the BiGRU and CNN modules are responsible for capturing global and local relationships among amino acids, and the combined application of the three pooling methods enhances the model's ability to capture critical information. Experimental validation shows that the CRULoc model outperforms several current advanced prediction models across various tasks. These results clearly demonstrate the superior performance of the CRULoc model in predicting the subcellular localization of eukaryotic proteins, providing robust support for the in-depth exploration of protein functions and mechanisms. © 2024 ieee.

关键词： Convolutional neural networks

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SIGNN - Star Identification Using Graph Neural networks

SIGNN - Star Identification Using Graph Neural Networks

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

作者： Hepburn-Dickins, Floyd Jones, Mark W. Edwards, Mike Morgan, Jay Paul Bell, Steve Swansea University United Kingdom Uk Hydrographic Office Somerset Taunton United Kingdom

ISBN: (纸本)9798331510831

As a solution for the lost-in-space star identification problem we present Star Identification using Graph Neural Network (SIGNN), a novel approach using Graph Attention networks. By representing the celestial sphere as a graph data structure, created from the ESA's Hipparcos catalogue, we are able to accurately capture the rich information and relationships within local star fields. Graph learning techniques allow our model to aggregate information and learn the relative importance of the nodes and structure within each stars local neighbourhood to it's identification. This approach, combined with our parametric data-generation and noise simulation, allows us to train a highly robust model capable of accurate star identification even under intensive noise, outperforming existing methods. Code and generation techniques will be available on https://***/FloydHepburn/SIGNN. © 2025 ieee.

关键词： Graph neural networks

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IFA-Net: Isomerous Feature-aware Network for Single-view 3D Reconstruction

IFA-Net: Isomerous Feature-aware Network for Single-view 3D ...

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International Joint conference on Neural networks (IJCNN)

作者： Zhang, Zecheng Han, Xianfeng Xiao, Guoqian Southwest Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9781665488679

Single-view 3D reconstruction has long been an intractable and fundamental problem in computer vision. Objects with complex topological structures are difficult to be accurately reconstructed, which makes the existing methods suffer from blurred shape boundaries between multiple components in the object. Recently, convolutional neural network and vision transformer have begun to appear in the field of 3D reconstruction and have been widely used with excellent performance. However, the existing transformer-based methods mainly focus on the global long-term context dependency, and ignore the local details of the part space features, resulting in poor reconstruction of the detail part. In this paper, we propose a novel dual-branch network architecture, called IFA-Net, to capture local spatial perception information and retain global structural features for singleview 3D reconstruction. In addition, we propose an isomerous feature-aware module, which enables the dynamic fusion of different resolution features under the two branches. Thus, high-fidelity and detail-rich 3D object reconstruction can be achieved. Extensive experimental results demonstrate that our method is able to produce high-quality voxels, particularly with diverse topologies, as compared with the state-of-the-art methods.

关键词： 3D Reconstruction Single-view Vision transformer Convolutional neural networks Feature-aware

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