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 ...

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, ...

Want to see what agentic coding really feels like? Come along on my wild, wonderful, and terrifying two-day vibe coding adventure with Xcode 26.3.

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 ...

Impulse AI is building an autonomous machine learning engineer that turns data into production models from a simple prompt. Founded in 2025 and based in California, the company enables teams to build, ...

Aims: To develop and validate a multi-feature machine learning (ML) model for early diabetic nephropathy (DN) prediction in elderly living with type 2 diabetes mellitus (T2DM), incorporating clinical ...

Abstract: Birth weight is a vital indicator of pregnancy progress and infant development. Accurate pre-delivery estimation is crucial, influencing both short- and long-term health outcomes. This study ...

1 Department of Geomatic Engineering, University of Mines and Technology, Tarkwa, Ghana. 2 Department of Geomatic and Civil Engineering, University of Mines and Technology, Essikado, Ghana. Accurate ...

WASHINGTON – The U.S. Army has established a new career pathway for officers to specialize in artificial intelligence and machine learning (AI/ML), formally designating the 49B AI/ML Officer as an ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...