AZoSensors on MSN

AI Maps Heat Inside Steelmaking’s Critical Sintering Process Beds

The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical ...
Tech Xplore on MSN

Machine learning helps robots see clearly in total darkness using infrared

From disaster zones to underground tunnels, robots are increasingly being sent where humans cannot safely go. But many of ...

Machine learning model predicts protein binding on gold nanoclusters

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...

New machine-learning models capture the rapid evolution of antimicrobial resistance

Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, ...
CoinDesk

AI trade isn’t dead: An inside look into Wall Street's lucrative data center deals

Megawatts are still trading hands, and the AI trade is very much alive, according to investment banker Joe Nardini, as miners ...
Microsoft

Access Fabric: A modern approach to identity and network access

Learn how an Access Fabric can continuously decide who can access what, from where, and under what conditions—in real time.
Communications of the ACM

Collective Intelligence Doesn’t Scale, In-Context Collaborative Learning Does

This is where Collective Adaptive Intelligence (CAI) comes in. CAI is a form of collective intelligence in which the ...
Scientific Research Publishing

Reinforcement Learning for Antidepressant Dose Adjustment: An Explainable Agent Approach ()

Reinforcement Learning, Explainable AI, Computational Psychiatry, Antidepressant Dose Optimization, Major Depressive Disorder, Treatment Personalization, Clinical Decision Support Share and Cite: de ...
Frontiers

AI-Driven Operations in Communication Networks

Artificial Intelligence (AI) and Machine Learning (ML) are transforming communication networks into intelligent, adaptive and autonomous systems. By ...
Scientific Research Publishing

Multimodal Digital Phenotyping for Bipolar Disorder: Robust Mood-State Classification and Early Relapse Risk Monitoring ()

Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...