A new study suggests that lenders may get their strongest overall read on credit default risk by combining several machine learning models rather than relying on a single algorithm. The researchers ...

This proposal outlines a machine learning-based approach aimed at improving productivity in haulage operations within open-pit mining. Since hauling accounts for up to 60% of total operational costs, ...

Researchers report that the integration of machine learning and Internet of Things (IoT) technologies is enabling a new generation of intelligent industrial environments capable of real-time ...

A study published in The Journal of Engineering Research (TJER) at Sultan Qaboos University presents an advanced intrusion detection system (IDS) designed to improve the accuracy and efficiency of ...

Accurate assessment of soil salinity is critical for sustainable agriculture and food security, yet remains technically challenging at fine spatial scales.

New paired studies from the University of Minnesota Twin Cities show that machine learning can improve the prediction of floods. The studies, published in Water Resources Research and the Proceedings ...

Research shows that combining silica fume, fly ash, and manufactured sand in concrete significantly boosts strength and enhances predictive modeling accuracy.

Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant risk of progressive renal impairment in a subset of patients. Early ...

MLIP calculations successfully identify suitable dopants for a novel photocatalytic material, report researchers from the ...

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular ...

Researchers develop a 96% accurate AI-powered retinal scan to distinguish between Alzheimer’s and ALS by detecting specific protein deposits.