Researchers at The University of Texas at Arlington have developed a new computational tool that helps scientists pinpoint ...

Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces ...

AI involves a wide range of techniques and approaches, including machine learning, deep learning, natural language processing, computer vision, and robotics. AI has significant applications across ...

What if people could detect cancer and other diseases with the same speed and ease as a pregnancy test or blood glucose meter ...

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design ...

For over a decade, researchers have considered boson sampling—a quantum computing protocol involving light particles—as a key ...

Density functional theory is a widely used computer-based quantum mechanical method for calculating properties of atoms, ...

Machine learning and AI projects require large amounts of data, which can quickly exceed the storage capacity of traditional databases. Scaling up the database infrastructure to accommodate the ...

Health research is increasingly turning to high-throughput molecular datasets (also known as ‘omic’ datasets) to discover novel biomarkers of disease risk and outcome. Unfortunately, the size and ...