Unite.AI

AI Breakthrough Improves Brain–Computer Interfaces by Decoding Complex Brain Signals

Researchers at Chiba University in Japan have developed a new artificial intelligence framework capable of decoding complex brain activity with significantly improved accuracy, marking an important ...
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 ...
Seeking Alpha

MicroCloud Hologram Inc. Releases Learnable Quantum Spectral Filter Technology for Hybrid Graph Neural Networks

SHENZHEN, China, Jan. 5, 2026 /PRNewswire/ -- MicroCloud Hologram Inc. (HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for ...
IEEE

Spatial–Spectral Graph Convolutional Network for Hyperspectral Target Detection

Abstract: Deep-learning-based hyperspectral target detection methods commonly face challenges such as insufficient target samples and inadequate use of spatial context. To address these limitations, ...
Scientific Research Publishing

An In-Depth Analysis of Graph Neural Networks and Machine Learning Approaches for Drug Repositioning ()

1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...