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 ...

Advances in artificial intelligence (AI) are now opening new possibilities for faster and more accurate flood mapping, ...

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 ...

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

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

Balancing nitrogen use is critical for maximizing crop yield while minimizing environmental and economic costs. A new approach integrates drone-based ...