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 ...

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 biggest risk is not taking any risk ... the only strategy that is guaranteed to fail is not taking risks," advised Mark Zuckerberg.Every story has a beginning. Every story has an element of risk.

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.

To use this evidence, investigators typically must grow the larvae until adulthood in a laboratory setting and then identify ...

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 ...

Abstract: Wireless Sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge ...

Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.