Results 71 to 80 of about 1,157 (178)

Lightweight Hybrid Wafer Defect Pattern Network Based on Feedforward Efficient Attention

CAAI Transactions on Intelligence Technology, Volume 11, Issue 1, Page 149-166, February 2026.
ABSTRACT With the increase of semiconductor integration density, in order to cope with the increase of wafer defect complexity and types, especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects, this study proposes a lightweight model for wafer defect detection called LightWMNet.
Zhiqiang Hu, Yiquan Wu
wiley   +1 more source

Stability Analysis of Different Quaternion‐Valued Impulsive BAM Neural Networks With Unknown Parameters and Time‐Varying Delays

Engineering Reports, Volume 8, Issue 1, January 2026.
This work tackles the unresolved stability problem of heterogeneous quaternion‐valued BAM neural networks plagued by unknown parameters, time‐varying delays, and impulses. By synergizing Lyapunov theory with inequality techniques, we establish rigorous, yet practical, global stability conditions.
Xi Long, Yaqin Li
wiley   +1 more source

EasyHypergraph: an open-source software for fast and memory-saving analysis and learning of higher-order networks

Humanities & Social Sciences Communications
Higher-order relationships exist widely across different disciplines. In the realm of real-world systems, significant interactions involving multiple entities are common.
Bodian Ye, Min Gao, Xiu-Xiu Zhan, Xinlei He, Zi-Ke Zhang, Qingyuan Gong, Xin Wang, Yang Chen   +7 more
doaj   +1 more source

NE-DCHL: Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning for Next Point-of-Interest Recommendation

Information
Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs)
Hongwei Zhang, Guolong Wang, Xiaofeng Yan   +2 more
doaj   +1 more source

HyMePre: A Spatial–Temporal Pretraining Framework with Hypergraph Neural Networks for Short-Term Weather Forecasting

Applied Sciences
Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making.
Fei Wang, Dawei Lin, Baojun Chen, Guodong Jing, Yi Geng, Xudong Ge, Daoming Wei, Ning Zhang   +7 more
doaj   +1 more source

Counterfactual Explanations for Hypergraph Neural Networks

CoRR
Hypergraph neural networks (HGNNs) effectively model higher-order interactions in many real-world systems but remain difficult to interpret, limiting their deployment in high-stakes settings. We introduce CF-HyperGNNExplainer, a counterfactual explanation method for HGNNs that identifies the minimal structural changes required to alter a model's ...
Fabiano Veglianti, Lorenzo Antonelli, Gabriele Tolomei   +2 more
openaire   +2 more sources

Hypergraph Neural Networks for Coalition Formation Under Uncertainty

Algorithms
Identifying effective coalitions of agents for task execution within large multiagent settings is a challenging endeavor. The problem is exacerbated by the presence of coalitional value uncertainty, which is due to uncertainty regarding the values of ...
Gerasimos Koresis, Charilaos Akasiadis, Georgios Chalkiadakis   +2 more
doaj   +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

Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation

IEEE Access
Hypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that ...
Hongji Shu, Chaojun Meng, Pasquale De Meo, Qing Wang, Jia Zhu   +4 more
doaj   +1 more source

Learning Directed Knowledge Using Higher-Ordered Neural Networks: Building a Predictive Framework

Applied Sciences
Most graph learning methods remain limited to undirected, pairwise interactions, restricting their ability to capture the multi-entity and directional relationships common in real-world systems. We propose the Directed Higher-Ordered Neural Network (HONN)
Yousra Moh Ousellam, Bikram Pratim Bhuyan, Rachida Fissoune, Galina Ivanova, Amar Ramdane-Cherif   +4 more
doaj   +1 more source