Results 111 to 120 of about 9,987 (195)

MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features. [PDF]

BMC Med Inform Decis Mak, 2021
Wang YT, Wu QW, Gao Z, Ni JC, Zheng CH.
europepmc   +1 more source

Attention-Based Hypergraph Neural Network: A Personalized Recommendation

Applied Sciences
Personalized recommendation for online learning courses stands as a critical research topic in educational technology, where algorithmic performance directly impacts learning efficiency and user experience.
Peihua Xu, Maoyuan Zhang
doaj   +1 more source

Spectral–Spatial Hypergraph Convolutional Network for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, due to the high-order nonlinear modeling capability of hypergraph convolutional network (HGCN), it has been introduced into the field of hyperspectral image (HSI) classification.
Qingwang Wang, Fenlian Liu, Mingguo Wang, Lizhi Wang, Jiangbo Huang, Tao Shen   +5 more
doaj   +1 more source

Unsupervised hyperspectral images classification using hypergraph convolutional extreme learning machines

IET Image Processing
Aiming at the problem that traditional methods are difficult to fully utilize the rich spectral information in hyperspectral images (HSI) and fail to capture the complex higher‐order relations in hyperspectral data, which leads to limited classification ...
Hongrui Zhang, Hongfei Lv, Mengke Wang, Luyao Wang, Jinhuan Xu, Fenggui Wang, Xiangdong Li   +6 more
doaj   +1 more source

Adaptive Expansion for Hypergraph Learning

Hypergraph, with its powerful ability to capture higher-order relationships, has gained significant attention recently. Consequently, many hypergraph representation learning methods have emerged to model the complex relationships among hypergraphs. In general, these methods leverage classic expansion methods to convert hypergraphs into weighted or ...
Ma, Tianyi, Qian, Yiyue, Zhang, Shinan, Zhang, Chuxu, Ye, Yanfang   +4 more
openaire   +2 more sources

Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism

Applied Sciences
Knowledge tracing (KT), a core educational data mining task, models students’ evolving knowledge states to predict future learning. In online education systems, the exercises are numerous, but they are typically associated with only a few concepts ...
Fanglan Ma, Changsheng Zhu, Peng Lei
doaj   +1 more source

Hierarchical Feature Interaction Learning for Accurate Tire Defect Identification: A Hypergraph-Enhanced Multi-Feature Ranking Technique

IEEE Access
In this paper, we present the Hierarchical Interaction Deep Learning (HIDL) framework, a unified approach for high-precision tire defect detection. The HIDL framework employs multimodal visual quality-guided hierarchical feature fusion, synergizing deep ...
Hou Ya-He, Yao Liang, Andrea Zucheli
doaj   +1 more source

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

Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs

Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed hypergraphs, which can model oriented group interactions, remains largely under-explored despite its relevance for many applications. Recent approaches in this direction often
Mule, Emanuele, Fiorini, Stefano, Purificato, Antonio, Siciliano, Federico, Coniglio, Stefano, Silvestri, Fabrizio   +5 more
openaire   +2 more sources

Multimodal deep learning on hypergraphs

, 2022
This thesis investigates the potential of hypergraphs for capturing higher-order relations between objects in a multimodal dataset. These relations are often sub-optimally represented by pairwise connections used in a graph. Hence, in order to unlock the full potential of relational information within a multimodal dataset, this thesis proposes several ...
openaire   +1 more source