Results 51 to 60 of about 1,216 (173)

Wasserstein Hypergraph Neural Network

CoRR
The ability to model relational information using machine learning has driven advancements across various domains, from medicine to social science. While graph representation learning has become mainstream over the past decade, representing higher-order relationships through hypergraphs is rapidly gaining momentum.
Iulia Duta, Pietro Liò
openaire   +2 more sources

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

Hyper-Ordinal Pattern: Measuring High-Order Connection Relationship in Brain Disease Networks

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Brain hyper-networks as a kind of hypergraph for brain network analysis, describing the high-order interactions among brain regions, have been extensively utilized in research on brain diseases such as mild cognitive impairment (MCI) and Alzheimer’
Tianyu Du, Kai Ma, Xibei Yang, Daoqiang Zhang   +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

2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction

International Journal of Digital Multimedia Broadcasting
Predicting origin-destination (OD) flow presents a significant challenge in intelligent transportation due to the intricate dynamic correlations between starting points and destinations.
Cheng Fang, Li Wang
doaj   +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