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 ...

Recently, model-based reinforcement learning has been considered a crucial approach to applying reinforcement learning in the physical world, primarily due to its efficient utilization of samples.

The severity of symptoms in posttraumatic stress disorder (PTSD) varies greatly across individuals in the first year after trauma and it remains difficult to predict whether someone might worsen, ...

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

A data science course is meant to equip learners with a higher level of positions that are founded on the analysis of data, statistics, and machine learning aimed at addressing intricate problems. The ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for clean-energy reactions are screened, identified, and validated across ...

Investors make predictable mistakes when forecasting earnings and stock returns, but machine learning models avoid them through adaptive learning.

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

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