Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

The Opioid Risk Tool for Opioid Use Disorder may help identify patients with chronic noncancer pain at increased risk for OUD ...

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...

AI agents help businesses stop guessing — linking predictions to actions so teams can move from “what might happen” to ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

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, ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...

Using the city of Lahore, Pakistan, as a detailed case study, the research offers broader insights into why smog remains so persistent in densely populated cities with high transport demand and ...

Financial word of the day: Heteroscedasticity describes a situation where risk (variance) changes with the level of a variable. In financial models, this means volatility is not constant. Most pricing ...