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

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

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 ...

The triage guide is intended to make sure patients get appropriate care, especially with the rise in telehealth and ...

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

Microgrids play a growing role in modern power systems, supporting renewable integration, local resilience, and decentralized ...

Data Systems Redefining Competitive Decision Making Professional sports are no longer shaped only by instinct, tradition, or experience passed down through coaching trees. Over the last decade, ...

Researchers have developed a powerful machine learning framework that can accurately predict and optimize biochar production from algae, offering a ...

Recent study reveals machine learning's potential in predicting the strength of carbonated recycled concrete, paving the way ...

Companies investing in AI initiatives succeed by focusing on real business value, strong data readiness, and securing employee buy in across teams.

A new topology-based method predicts atomic charges in metal-organic frameworks from bond connectivity alone, making large-scale computational screening practical.