A NIMS research team has developed a new experimental method capable of rapidly evaluating numerous material compositions by measuring anomalous Hall resistivity 30 times faster than conventional ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

The future of work isn’t human vs. machine — it’s smart teams blending both. Here’s how to build your edge in an AI-powered ...

RIT astrophysicist uses AI and machine learning to hunt for systems that will lead to understanding how stars live, die, and ...

Explore campus events in Tiruchi, highlighting art competitions, heritage expos, AI workshops, and educational initiatives ...

Analyzing current trends allows experts to predict how cybercriminals will leverage artificial intelligence in the future.

An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...

A research team from the Songshan Lake Materials Laboratory has developed an AI-guided "Recommendation System" to discover ...

You can train your own image models for deployment on an ESP32-S3, and it's really easy.

In the context of quantum computing, open source platforms allow developers, researchers, and organisations to collaborate, share resources, and build on existing tools without the need for costly ...

Magnetic sensors quietly underpin everything from smartphone compasses to the stability systems that keep electric vehicles ...

In today's hyper-connected global economy, data streams never sleep, models proliferate, and pundits proclaim certainty ...