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 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 ...

A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

A study published in The Journal of Engineering Research 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.

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

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

Reported accuracies were 86% (Random Forest) and 96% (convolutional neural networks), positioning retinal imaging as a candidate scalable tool for underserved populations. AI-powered polarized-light ...

Chemical toxicity evaluation is vital in the medical, industrial, and agricultural sectors to ensure rigorous safety testing and to prevent harmful ...

Dr. Melanie Campbell and graduate student Lyndsy Acheson study an image of a retina. They are looking for protein deposits found in association with brain diseases, such as Alzheimer's, FTLD-TDP and ...