Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...

Interpretable neural networks help reveal the nature of dark matter

A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an ...
EurekAlert!

Metallic glass discovery using AI-guided graph learning from Wikipedia data

A research team from the Songshan Lake Materials Laboratory has developed an AI-guided "Recommendation System" to discover new metallic glasses (MG). By combining element embeddings learned from ...
IEEE

RNGCNs: Robust Norm Graph Convolutional Networks in the Presence of Missing Data and Large Noises

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...
IEEE

Temporal Temperature Profile Prediction Using Graph Convolutional Networks and Inverted Echosounder Measurements

Abstract: Ocean temperature prediction is a prominent research topic in current ocean science. The empirical modal method, which is based on the inverse echosounder, is one of the most significant ...