Results 81 to 90 of about 1,157 (178)

STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis

Aerospace
Accurate fault diagnosis in aerospace transmission systems is essential for ensuring equipment reliability and operational safety, especially for aero-engine bearings.
Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Boon Xian Chai   +4 more
doaj   +1 more source

Betweenness Approximation for Edge Computing with Hypergraph Neural Networks

Tsinghua Science and Technology
Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things (IoTs), given that various IoT systems have been generating big data to facilitate modern latency-sensitive applications.
Yaguang Guo, Wenxin Xie, Qingren Wang, Dengcheng Yan, Yiwen Zhang   +4 more
doaj   +1 more source

DPHGNN: A Dual Perspective Hypergraph Neural Networks

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Accepted in SIGKDD'24 -- Research ...
Siddhant Saxena, Shounak Ghatak, Raghu Kolla, Debashis Mukherjee, Tanmoy Chakraborty 0002   +4 more
openaire   +2 more sources

Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention

Applied Sciences
Hypergraph analysis extends traditional graph theory by enabling the study of complex, many-to-many relationships in networks, offering powerful tools for understanding brain connectivity.
Alexander N. Pisarchik, Natalia Peña Serrano, Walter Escalante Puente de la Vega, Rider Jaimes-Reátegui   +3 more
doaj   +1 more source

Multivariate Time Series Anomaly Detection Using Directed Hypergraph Neural Networks

Applied Artificial Intelligence
Multivariate time series anomaly detection is a challenging problem because there can be a number of complex relationships between variables in multivariate time series. Although graph neural networks have been shown to be effective in capturing variable-
Tae Wook Ha, Myoung Ho Kim
doaj   +1 more source

Dual convolutional network based on hypergraph and multilevel feature fusion for road extraction from high-resolution remote sensing images

International Journal of Digital Earth
Road extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional network based
BoWen Li, XiangHong Tang, Rang Xiao, JianGuang Lu, YuHao Wang   +4 more
doaj   +1 more source

HGSNet: A hypergraph network for subtle lesions segmentation in medical imaging

IET Image Processing
Lesion segmentation is a fundamental task in medical image processing, often facing the challenge of subtle lesions. It is important to detect these lesions, even though they can be difficult to identify.
Junze Wang, Wenjun Zhang, Dandan Li, Chao Li, Weipeng Jing   +4 more
doaj   +1 more source

Hyperedge Anomaly Detection with Hypergraph Neural Network

CoRR
Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications.
Md. Tanvir Alam, Chowdhury Farhan Ahmed, Carson K. Leung   +2 more
openaire   +2 more sources

Hypergraph Neural Networks Accelerate MUS Enumeration

CoRR
Enumerating Minimal Unsatisfiable Subsets (MUSes) is a fundamental task in constraint satisfaction problems (CSPs). Its major challenge is the exponential growth of the search space, which becomes particularly severe when satisfiability checks are expensive.
Hiroya Ijima, Koichiro Yawata
openaire   +2 more sources

ERP Insights and Truncated SVD in Conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis

International Journal of Computational Intelligence Systems
Multi-modal EEG data analysis requires sophisticated methods for accurate prediction in the critical area of cognitive depression study in neuroscience.
N. Banupriya, Neelakandan Subramani, M. Prakash, S. Velmurugan   +3 more
doaj   +1 more source