In 2026, here's what you can expect from the AI industry: new architectures, smaller models, world models, reliable agents, ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

Manufacturing technologies have been the first domain to experience this transformation. The review documents how artificial ...

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

Researchers at National University of Singapore used multiple interpretable machine learning methods to predict traffic congestion in in Alameda ...

A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an interpretable artificial intelligence (AI) framework named Convolutional Kolmogorov ...

The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical challenges in steelmaking processes.

In this tutorial, we walk through an advanced end-to-end data science workflow where we combine traditional machine learning with the power of Gemini. We begin by preparing and modeling the diabetes ...

ABSTRACT: Atrial fibrillation (AF) is a leading cardiac arrhythmia associated with elevated mortality risk, particularly in low-resource settings where early risk stratification remains challenging.

What if the key to unlocking faster, more efficient machine learning workflows lies not in your algorithms but in the hardware powering them? In the world of GPUs, where raw computational power meets ...

Abstract: This study compared several machine learning algorithms to predict student programming learning performance in an online learning environment. It used Explainable Machine Learning (EML) ...