Read more about AI-driven learning analytics struggle to deliver measurable gains in higher education on Devdiscourse ...

Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

Researchers at the Department of Energy's Oak Ridge National Laboratory have developed a deep learning algorithm that ...

Serial CT response score (CTRS) predicted overall survival (OS) more effectively than existing imaging-based measures in patients with advanced non–small cell lung cancer (NSCLC) receiving immune ...

South Korean researchers have developed a guided-learning framework that accurately predicts PV power without requiring irradiance sensors during operation, using routine meteorological data instead.

A new study finds that humans and AI spot different kinds of deepfakes — hinting at the need to team up to fight them.

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.

Calsoft introduced an AI-powered approach to Test Impact Analysis that eliminates unnecessary test executions in CI/CD ...

Can deep learning catch chronic illness before symptoms show? This article explores how time-aware neural networks are reshaping early detection and care planning for conditions like diabetes and COPD ...

AI autoscaling promises a self-driving cloud, but if you don’t secure the model, attackers can game it into burning cash or ...