HyperGraph-based capsule temporal memory network for efficient and explainable diabetic retinopathy detection in retinal imaging [PDF]
Scientific ReportsDiabetic retinopathy (DR) is a chronic complication of diabetes in which the retinal damage may cause vision impairment or blindness if left untreated. The challenges in DR detection are mostly due to the morphological variations of retinal lesions, e.g.,
Mishmala Sushith +3 more
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A spatiotemporal hypergraph self-attention neural networks framework for the identification and pharmacological efficacy assessment of Parkinson’s disease motor symptoms [PDF]
npj Parkinson's DiseaseL-DOPA-induced dyskinesia (LID) is a common complication in the treatment of Parkinson’s disease (PD), characterized by involuntary excessive movements. The traditional Abnormal Involuntary Movement Scale (AIMs), used for quantifying abnormal involuntary
Xiaochen An +7 more
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Hypergraph-based contrastive learning for enhanced fraud detection [PDF]
Frontiers in Artificial IntelligenceThe proliferation of digital platforms has enabled fraudsters to deploy sophisticated camouflage techniques, such as multi-hop collaborative attacks, to evade detection.
Qinhong Wang, Yiming Shen, Husheng Dong
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Directed Hypergraph Neural Network [PDF]
Journal of Advanced Research in Dynamical and Control Systems, 2020 To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we
Tran, Loc Hoang, Tran, Linh Hoang
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A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images
Remote Sensing, 2023 The very high spatial resolution (VHR) remote sensing images have been an extremely valuable source for monitoring changes occurring on the Earth’s surface. However, precisely detecting relevant changes in VHR images still remains a challenge, due to the
Junzheng Wu +6 more
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Multi-site Hyper-graph Convolutional Neural Networks and Application [PDF]
Jisuanji kexue, 2022 Recently,the exploitation of graph neural networks for neurological brain disorder diagnosis has attracted much attention.However,the graphs used in the existing studies are usually based on the pairwise connections of different nodes,and thus cannot ...
ZHOU Hai-yu, ZHANG Dao-qiang
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Model of the malicious traffic classification based on hypergraph neural network
网络与信息安全学报, 2023 As the use and reliance on networks continue to grow, the prevalence of malicious network traffic poses a significant challenge in the field of network security.Cyber attackers constantly seek new ways to infiltrate systems, steal data, and disrupt ...
Wenbo ZHAO, Zitong MA, Zhe YANG
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Directed hypergraph attention network for traffic forecasting
IET Intelligent Transport Systems, 2022 In traffic systems, traffic forecasting is a critical issue, which has attracted much interest from researchers. It is a challenging task due to the complex spatial‐temporal patterns of traffic data.
Xiaoyi Luo, Jiaheng Peng, Jun Liang
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Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation
Data Science and Engineering, 2023 Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li +3 more
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Equivariant Hypergraph Neural Networks
, 2022 Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations. Recent approaches for hypergraph learning extend graph neural networks based on message passing, which is simple yet fundamentally limited in modeling long-range dependencies and expressive power. On the other hand, tensor-
Kim, Jinwoo +3 more
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