Video Anomaly Detection: A Systematic Review of Issues and Prospects
The increase in the deployment of surveillance camera in outdoor and indoor settings have
resulted in a growing demand for intelligent systems that can accurately detect and …
resulted in a growing demand for intelligent systems that can accurately detect and …
Multi-view graph contrastive learning via adaptive channel optimization for depression detection in EEG signals
S Zhang, H Wang, Z Zheng, T Liu, W Li… - … Journal of Neural …, 2023 - World Scientific
Automated detection of depression using Electroencephalogram (EEG) signals has become
a promising application in advanced bioinformatics technology. Although current methods …
a promising application in advanced bioinformatics technology. Although current methods …
GCNs–FSMI: EEG recognition of mental illness based on fine-grained signal features and graph mutual information maximization
W Li, H Wang, L Zhuang - Expert Systems With Applications, 2023 - Elsevier
There is growing evidence that an increasing number of people suffer from mental illness,
which seriously affects their quality of life. The study of electroencephalography (EEG) is …
which seriously affects their quality of life. The study of electroencephalography (EEG) is …
ISTGCN: Integrated spatio-temporal modeling for traffic prediction using traffic graph convolution network
To effectively estimate traffic patterns, spatial-temporal information must consider the
complex spatial connections on road networks and time-dependent traffic information …
complex spatial connections on road networks and time-dependent traffic information …
Sparse graph cascade multi-kernel fusion contrastive learning for microbe–disease association prediction
Predicting microbe–disease associations (MDA) is crucial for proactively demystifying
diseases causes and preventing them. Traditional prediction methods endure labor …
diseases causes and preventing them. Traditional prediction methods endure labor …
[HTML][HTML] EDDINet: Enhancing drug–drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning
Predicting drug–drug interactions (DDIs) is crucial for understanding and preventing
adverse drug reactions (ADRs). However, most existing methods inadequately explore the …
adverse drug reactions (ADRs). However, most existing methods inadequately explore the …
EMPPNet: Enhancing Molecular Property Prediction via Cross-modal Information Flow and Hierarchical Attention
Obtaining comprehensive and informative representations of molecules is a crucial
prerequisite for efficient molecule property prediction in artificial intelligence-driven drug …
prerequisite for efficient molecule property prediction in artificial intelligence-driven drug …
Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reaction
Abstract Adverse Drug Reactions (ADRs) resulting from drug combinations can endanger
patients' health and life. In this paper, we propose ADGCL, a novel Adaptive Dual Graph …
patients' health and life. In this paper, we propose ADGCL, a novel Adaptive Dual Graph …
Local-Global Graph Fusion to Enhance scRNA-seq Clustering
L Du, Y Han - IEEE Access, 2024 - ieeexplore.ieee.org
Single-cell RNA sequencing (scRNA-seq) is crucial for demystifying the cell heterogeneity
and differentiation processes, enabling the identification of distinct cell subtypes within a …
and differentiation processes, enabling the identification of distinct cell subtypes within a …
A Novel Hybrid Model Detection of Security Vulnerabilities in Industrial Control Systems and IoT Using GCN+ LSTM
In this study, we address critical security vulnerabilities in Industrial Control Systems (ICS)
and the Internet of Things (IoT) by focusing on enhancing collaboration and communication …
and the Internet of Things (IoT) by focusing on enhancing collaboration and communication …