Soft Computing (SC) is an Artificial Intelligence (AI) approach that is more effective at solving real-life problems than traditional computing models. Soft Computing models are tolerant of partial ...

Ceramic's Supervised Generation augments LLM outputs with search grounding, citations and confidence signals -- bringing verifiable, trustworthy AI to enterprise applications. -- NVIDIA Nemotron 3 ...

Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational psychiatry. Real-world datasets from smartphones and wearables often suffer from ...

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.

Objective To estimate the prevalence of potential overtreatment of type 2 diabetes mellitus (T2DM) among older adults and to develop and compare predictive models to identify patient and physician ...

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

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 ...

Self-supervised reinforcement learning is a technique where agents learn useful representations and skills from the environment through self-generated tasks, such as predicting next states or learning ...