Results 41 to 50 of about 1,157 (178)

MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model

AI Open, 2023
Stock price prediction is challenging in financial investment, with the AI boom leading to increased interest from researchers. Despite these recent advances, many studies are limited to capturing the time series characteristics of price movement via ...
Zhongtian Sun, Anoushka Harit, Alexandra I. Cristea, Jingyun Wang, Pietro Lio   +4 more
doaj   +1 more source

Land Use and Land Cover Analysis for Solid Waste Management in NCT Delhi Using Fusion Aware Segformer

Land Degradation &Development, EarlyView.
ABSTRACT The use of Land Use Land Cover (LULC) analysis is a fundamental requirement for urban solid waste management (SWM); however, conventional LULC analysis methods are not well suited to the spatio‐temporal variability, multi‐sensor heterogeneity, and seasonal variations of highly dynamic urban environments.
Rubeena Vohra, Ashish Kumar
wiley   +1 more source

Hypergraph Neural Networks Based on Enclosing Subgraph Extraction for Temporal Link Prediction

IEEE Access
The Graph Neural Network (GNN) methods based on enclosing subgraph extraction have achieved excellent results in static graph link prediction tasks. However, most real-world networks are dynamic and evolve over time; the traditional models cannot capture
Ying Zhao, Xiangxu Meng, Liang Chen, Junjun Chen, Atul Sajjanhar   +4 more
doaj   +1 more source

A Novel Full‐Chain Loop Tracking Auditing Framework for Obtaining Audit Evidence With Reasonable Assurance

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT As an attestation engagement, auditing is required to provide reasonable assurance for its conclusions. Traditional auditing has limited capacity to handle unstructured data and is usually based on audit sampling techniques, which can lead to the neglect of important audit evidence during the auditing process and result in a higher audit risk,
Xiaojia Wang, Ziqing Luo, Chaoxu Mu
wiley   +1 more source

DGHSA: derivative graph-based hypergraph structure attack

Scientific Reports
Hypergraph Neural Networks (HGNNs) have been significantly successful in higher-order tasks. However, recent study have shown that they are also vulnerable to adversarial attacks like Graph Neural Networks. Attackers fool HGNNs by modifying node links in
Yang Chen, Zhonglin Ye, Zhaoyang Wang, Jingjing Lin, Haixing Zhao   +4 more
doaj   +1 more source

H2CD: Semantic‐Enhanced Heterogeneous Hypergraph Network With Large Language Model for Cognitive Diagnosis

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Cognitive diagnosis aims to infer learners' knowledge states from their exercise responses, enabling personalised education at scale. Existing methods represent exercises solely by coarse‐grained knowledge component annotations, overlooking semantic content and step‐level cognitive processes.
Youheng Bai, Zitao Liu, Mingliang Hou, Teng Guo, Weiqi Luo   +4 more
wiley   +1 more source

Single‐cell marker gene clustering: A unified deep learning framework for marker gene‐based clustering of single‐cell RNA‐sequencing data

Quantitative Biology, Volume 14, Issue 3, September 2026.
Abstract Single‐cell RNA sequencing (scRNA‐seq) has transformed the study of cellular heterogeneity by making it possible to classify individual cells and their functional states. However, the analysis remains difficult because high dropout rates lead to sparse and noisy expression data.
Shahriar Rahman Niloy, Toushif Muktashid Hasan, Md. Saiduzzaman Apu, Fahim Hafiz, Salekul Islam, Riasat Azim   +5 more
wiley   +1 more source

Hypergraph Representation Learning for Remote Sensing Image Change Detection

Remote Sensing
To address the challenges of change detection tasks, including the scarcity and dispersion of labeled samples, the difficulty in efficiently extracting features from unstructured image objects, and the underutilization of high-order correlation ...
Zhoujuan Cui, Yueran Zu, Yiping Duan, Xiaoming Tao   +3 more
doaj   +1 more source

GBT‐SAM: A Parameter‐Efficient Depth‐Aware Model for Generalizable Brain Tumor Segmentation on mp‐MRI

International Journal of Imaging Systems and Technology, Volume 36, Issue 4, July 2026.
ABSTRACT Gliomas are aggressive brain tumors that require accurate imaging‐based diagnosis, where automated segmentation plays a central role in assessing tumor morphology and guiding treatment decisions. Manual delineation of gliomas is time‐consuming and prone to variability, motivating the use of deep learning to improve consistency and alleviate ...
Cecilia Diana‐Albelda, Roberto Alcover‐Couso, Álvaro García‐Martín, Jesus Bescos, Marcos Escudero‐Viñolo   +4 more
wiley   +1 more source

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