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 ...

Advances in artificial intelligence (AI) are now opening new possibilities for faster and more accurate flood mapping, enabling researchers to process large volumes of environmental data and satellite ...

Machine learning reveals unique COVID vaccine immune signatures in people living with HIV, with differences in antibodies, cytokines, and T cell responses.

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 ...

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 ...

Domain adaptation may be a novel creative solution to predict infection risk in patients with chronic lymphocytic leukemia ...

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 ...

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 ...

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