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 ...

Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

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

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, ...

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 ...

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

Intel is looking for a Data Scientist who specializes in Demand and Supply Planning to develop advanced analytics and machine learning systems that will optimiz ...

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 ...

Heteroscedasticity describes a situation where risk (variance) changes with the level of a variable. In financial models, ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

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