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 ...

The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary ...

A machine learning-driven framework accurately predicts MPA exposure and supports individualized dosing in childhood-onset LN.

Biochar is widely promoted as a climate friendly soil amendment that can store carbon and improve crop growth. Yet scientists have long debated whether it always benefits soil ecosystems. A new study ...

Traditional lending relies on collateral and a financial history that productive smallholder farmers may find difficult to ...

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 ...

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 ...

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

In A Nutshell Researchers used a machine learning model to rank all 50 U.S. states and Washington, D.C. by socioeconomic vulnerability to flu-like illness, finding wide regional variation in risk.

A retinal image could help doctors quickly distinguish between similar neurodegenerative diseases such as ALS and Alzheimer's disease, and with ...