Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

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 ...

Forecasting tropical weather is challenging, but new tools have emerged over the past couple of years that are proving beneficial. Thank machine learning. The European Center for Medium-Range Weather ...

The authors analyze the interest rate risk in the banking book regulations, arguing that financial institutions must develop robust models for forecasting ...

BOULDER, Colo. – OpenSnow, a trusted source for the most accurate U.S. weather forecasts, snow reports, and AI-powered weather maps, is launching first-of-their-kind new tools this winter that ...

Researchers in China conceived a new PV forecasting approach that integrates causal convolution, recurrent structures, attention mechanisms, and the Kolmogorov–Arnold Network (KAN). Experimental ...

The absence of reliable data on fundamental economic indicators (e.g. real GDP), combined with structural shifts in the economy, can severely constrain the ability to conduct accurate macroeconomic ...

As instigators of immunity, monoclonal antibodies are marvels of modern medicine, lab-made proteins that can treat cancers, ...

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...