Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

As the world grapples with the energy crisis and environmental concerns, the focus on renewable energy sources has intensified. Lithium-ion batteries, with their high energy density and low pollution, ...

Real-world data (RWD) is transforming clinical research, augmenting existing randomized controlled trial (RCT) data to de-risk studies and improve generalizability. With regulators setting clearer ...

As semiconductor technologies advance, device structures are becoming increasingly complex. New materials and architectures introduce intricate physical effects requiring accurate modeling to ensure ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Researchers from Radical AI have published a paper describing TorchSim, a next-generation open-source atomistic simulation engine built entirely in PyTorch. By rewriting the core primitives of ...

The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and ...

NTN announced that it has integrated machine learning technology into its automated calculation system used for designing 3rd‑generation hub bearings, marking the first use of this approach in the ...