Results 51 to 60 of about 1,157 (178)

Noise-robust classification with hypergraph neural network

Indonesian Journal of Electrical Engineering and Computer Science, 2021
<p>This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets
Dang, Nguyen Trinh Vu, Tran, Loc, Tran, Linh   +2 more
openaire   +3 more sources

CF‐SBERTHet: Collaborative and Textual Knowledge Enhanced Semantic Graphs for Sparse Recommendations

Expert Systems, Volume 43, Issue 7, July 2026.
ABSTRACT Modern e‐commerce platforms face a critical challenge: delivering accurate recommendations under extreme user–item interaction sparsity, where textual context remains systematically underutilised. Existing collaborative filtering methods degrade sharply in sparse settings, while semantic approaches fail to capture collaborative patterns ...
He Ma, Maryam Khanian Najafabadi, Qiyang Wu, WeiPing Kong, Huajun Jie, Fanye Sun, Xiaoyue Bai, Mohammadhossein Ahmadi   +7 more
wiley   +1 more source

Hyperbolic Hypergraph Neural Networks for Hierarchical Fault Diagnosis in Rotating Machinery

Sensors
Intelligent fault diagnosis of rotating machinery is essential for ensuring the safety and reliability of industrial systems. While hypergraph neural networks (HGNNs) have recently shown promise for modeling high-order dependencies beyond pairwise graph ...
Lingzheng Pan, Kyaw Hlaing Bwar, Rifai Chai, Yuqi Wang, Boon Xian Chai   +4 more
doaj   +1 more source

Hypergraph Convolutional Network with Multi-perspective Topology Refinement forSkeleton-based Action Recognition [PDF]

Jisuanji kexue
Since the human skeleton is a natural topological structure,graph convolutional networks(GCNs) are widely used for skeleton-based human action recognition.In recent research,skeleton sequences are represented as spatio-temporal graphs and topology graphs
HUANG Qian, SU Xinkai, LI Chang, WU Yirui
doaj   +1 more source

Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations

Applied Sciences, 2023
Emotion recognition in multi-party conversations (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Recently, many approaches have been devoted to exploiting inter-dependency and self-dependency among ...
Haojie Xu, Cheng Zheng, Zhuoer Zhao, Xiao Sun   +3 more
doaj   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, Volume 13, Issue 35, 24 June 2026.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration

Human Brain Mapping, Volume 47, Issue 8, June 1, 2026.
We propose MSSM+, an extension of multiscale structural mapping (MSSM), together with surface supervertex mapping (SSVM) and a Supervertex Vision Transformer (SV‐ViT). Together, these methods exhibited better performance in detecting Alzheimer's disease and less variability across MR vendors than MSSM.
Geonwoo Baek, David H. Salat, Ikbeom Jang, for the Alzheimer's Disease Neuroimaging Initiative   +3 more
wiley   +1 more source

A Dynamic Correlation‐Information‐Fusion‐Based Spatiotemporal Network for Traffic Flow Forecasting

CAAI Transactions on Intelligence Technology, Volume 11, Issue 3, Page 859-874, June 2026.
ABSTRACT Traffic Flow Forecasting (TFF) is a foundational task in the development of Intelligent Transport Systems (ITSs). The primary challenge is to undertake a comprehensive exploration of the intrinsic dynamic spatiotemporal correlations of the road network, unveiling the long‐term evolutionary traffic trends.
Dawen Xia, Zhan Lin, Xingyan Wang, Ruixi Huang, Jinhui Hu, Yang Hu, Huaqing Li   +6 more
wiley   +1 more source

HSL‐CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids

Energy Conversion and Economics, Volume 7, Issue 3, Page 207-220, June 2026.
Abstract An effective method for detecting cyberattacks is essential to the security of smart grids (SGs). In SGs, data from both cyber and physical domains can support attack detection. However, existing works insufficiently consider the heterogeneity, high dimensionality, and cross‐domain correlations of multi‐source data, affecting model ...
Qize Gao, Qiuyu Lu, June Li, Zhao Peng, Guo Shen, Ming Ni   +5 more
wiley   +1 more source

Single‐Cell and Spatial Omics: Methods and Applications

MedComm, Volume 7, Issue 4, April 2026.
Systematically summarized the breakthrough sequencing technologies and computational methods for single‐cell and spatial omics across multiple omics layers, including genome, epigenome, transcriptome, proteome, and metabolome. State‐of‐the‐art methods for multi‐omics integration, cross‐modal integration, and cross‐scale integration were reviewed, with ...
Xiaoping Cen, Xiaolan Huang, Enjin Deng, Xue Gong, Na Tan, Jifeng Ye, Yin Wang, Roland Eils, Qun Luo, Yixue Li, Fangfang Qu   +10 more
wiley   +1 more source