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A Hybrid Deep Learning Framework for Early Detection of Ovarian Cancer Using Ultrasound and MRI Images on a Secure Cloud Platform

Complexity, Volume 2026, Issue 1, 2026.
Ovarian cancer continues to pose a major diagnostic challenge, as early‐stage disease often presents with subtle and heterogeneous imaging characteristics that limit the effectiveness of single‐modality analysis. In response to this challenge, this study proposes a novel hybrid deep learning framework for the early detection and classification of ...
Umesh Kumar Lilhore, Vanusha D., Srilatha Gundapaneni, Anto Lourdu Xavier Raj Arockia Selvarathinam, Rasmi A., Sarita Simaiya, Lidia Gosy Tekeste, Ehab Seif Ghith, Heba G. Mohamed, Raul Alcaraz Martinez   +9 more
wiley   +1 more source

Adaptive Hypergraph Neural Network for Multi-Person Pose Estimation

, 2022
This paper proposes a novel two-stage hypergraph-based framework, dubbed ADaptive Hypergraph Neural Network (AD-HNN) to estimate multiple human poses from a single image, with a keypoint localization network and an Adaptive-Pose Hypergraph Neural Network
Xu, Xixia, Lin, Xue, Zou, Qi
core   +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

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

, 2021
Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role.
Zhang, Xiangliang, Wang, Qinyong, Cui, Lizhen, Yu, Junliang, Yin, Hongzhi, Xia, Xin   +5 more
core  

A Review of Hypergraph Neural Networks

EAI Endorsed Transactions on e-Learning
In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships.
openaire   +1 more source

RETRACTED: A Global Structural Hypergraph Convolutional Model for Bundle Recommendation

, 2023
Bundle recommendations provide personalized suggestions to users by combining related items into bundles, aiming to enhance users’ shopping experiences and boost merchants’ sales revenue.
Man Yuan, Xingtong Liu
core   +1 more source

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

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

Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions

, 2023
Predicting drug-drug interactions (DDI) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e.
Nguyen, Duc Anh, Mamitsuka, Hiroshi, Nguyen, Canh Hao   +2 more
core  

Music Recommendation via Hypergraph Embedding

, 2023
In recent years, we have witnessed an ever wider spread of multimedia streaming platforms (e.g., Netflix, Spotify, and Amazon). Hence, it has become more and more essential to provide such systems with advanced recommendation facilities, in order to ...
Moscato V., Sperli G., La Gatta V., Pennone M., Postiglione M.   +4 more
core   +1 more source