A Lightweight Self-Supervised Representation Learning Framework for Depression Risk Profiling from Synthetic Daily Behavioural Trajectories ...

An algorithm that finds lost civilizations is helping archaeologists use AI to predict where ancient sites may still be hidden.

The field of cardiovascular diagnostics is undergoing a transformative shift with the advent of artificial intelligence (AI)-enhanced electrocardiography ...

Shallem, Greg Ravikovich and Eitan Har-Shoshanim examine how AI addresses the challenge of data overload in solar PV.

Systematic human inspection of the millions of source cutouts in the Hubble Legacy Archive is impossible – but artificial ...

Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and limited ...

MIT researchers unveil a new fine-tuning method that lets enterprises consolidate their "model zoos" into a single, continuously learning agent.

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Abstract: Electroencephalogram (EEG) signals provide useful information for diagnosing sleep disorders such as restless legs syndrome, insomnia, snoring, and sleep hypoventilation. The use of an ...