Recent advancements in machine learning have substantially transformed the optimisation of the steelmaking process. Traditional methods, often limited by complex thermodynamic interactions and ...

Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...

TotalEnergies' deployment of machine learning at its Port Arthur, Texas, refinery demonstrates how predictive AI can ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.

A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), has ...

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