Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...

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 ...

Earth Scientists have used machine learning for at least three decades and the applications span is large, from remote sensing to analysis of well log data, among many others. Although machine ...

Neural networks are the backbone of algorithms that predict consumer demand, estimate freight arrival time, and more. At a high level, they're computing systems loosely inspired by the biological ...

Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...

Emergence of new applications and use cases: Neural networks are being applied to an increasingly diverse range of applications, including computer vision, natural language processing, fraud detection ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

The curious minds at ColdFusion explore how neural networks are shaping today’s AI applications.