Machine learning

A new diagnostic innovation powered by artificial intelligence could empower surgeons to make more accurate and effective clinical decisions for colon cancer patients.

A new cloud-based machine learning framework processes sensitive encrypted data 20 times faster than its predecessors.

Artificial intelligence could help speed up the discovery and development of materials towards a greener future.

Researchers have built a navigation algorithm that leverages multidimensional real-world data to map faster and more efficient traffic routes.

To fully realise the potential of medical technology, A*STAR scholar Daryl Jude Lawrence explains why scientists should move beyond the lab and experience the inner workings of industry-level production and entrepreneurship.

A new compression method developed at A*STAR shrinks energy and hardware requirements for complex computational platforms on everyday devices.

A purpose-built machine learning platform takes the guesswork out of catalyst formulations for green energy generation.

Computer scientists develop an algorithm that tracks moving objects faster and more accurately than current platforms.

A new neural network designed by A*STAR researchers can train algorithms to visually detect defective products in production lines with limited data.

A new machine learning model processes datasets from different sources simultaneously, paving the way for major advancements in medicine and beyond.

Materials science and computational techniques come together to advance new design schemes for aerospace alloys.

A new data selector helps more accurately train machine learning algorithms by recycling datasets from other applications.