A new artificial intelligence (AI) method called BioPathNet helps researchers systematically search large biological data ...
The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...
Major Depressive Disorder (MDD) is a leading cause of disability among adolescents, yet the efficacy of Electroconvulsive ...
Looking back at 2025, it’s obviously, on a daily basis, why the broadcast networks are dismissed by most Americans as a source of daily advertising for one side of the political debate. This tilt has ...
Efficient Channel Attention-Gated Graph Transformer for Aero-Engine Remaining Useful Life Prediction
The rapid technological progress in recent years has driven industrial systems toward increased automation, intelligence, and precision. Large-scale mechanical systems are widely employed in critical ...
Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances
Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
Abstract: Deep learning (DL) has been widely applied in wireless communications to address diverse challenges, such as beam management, positioning, and channel state information (CSI) feedback.
Abstract: Graph neural networks learn node embeddings by recursively sampling and aggregating nodes in a graph, while existing methods have a fixed pattern of node sampling and aggregation, and ...
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