Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Researchers have developed a new machine-learning-assisted approach to optimize micro-electro-discharge machining (µ-EDM) of ...

Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

Foreign exchange markets are shaped by liquidity fluctuations, which can trigger return volatility and price jumps. Identifying and predicting abnormal FX returns is critical for risk management and ...

When experiments are impractical, density functional theory (DFT) calculations can give researchers accurate approximations of chemical properties. The mathematical equations that underpin the ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

RIT researchers publish a paper in Nature Scientific Reports on a new tree-based machine learning algorithm used to predict chaos.

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...