IEEE

Intrusion Detection in IoT Networks Using Dynamic Graph Modeling and Graph-Based Neural Networks

Abstract: The rapid expansion of Internet of Things (IoT) networks has significantly increased security vulnerabilities, exposing critical infrastructures to sophisticated cyberattacks. Traditional ...
eLife

Inverted encoding of neural responses to audiovisual stimuli reveals super-additive multisensory enhancement

A multivariate analysis of electroencephalography activity reveals super-additive enhancements to the neural encoding of audiovisual stimuli, providing new insights into how the brain integrates ...
IEEE

Dynamical Causal Graph Neural Network for EEG Emotion Recognition

Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...