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

The process of testing new solar cell technologies has traditionally been slow and costly, requiring multiple steps. Led by a fifth-year PhD student, a Johns Hopkins team has developed a machine ...

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

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 ...

High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, traditional methods like the R-ratio ...

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

Read more about how machine learning and deep learning differ, where each is used, and how businesses choose between them in ...

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

Jordan Awan receives funding from the National Science Foundation and the National Institute of Health. He also serves as a privacy consultant for the federal non-profit, MITRE. In statistics and ...

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 ...