With large masses of data distributed across numerous storage centers, machine learning has been invaluable as Capital One ...

Data science and machine learning technologies have long been important for data analytics tasks and predictive analytical software. But with the wave of artificial intelligence and generative AI ...

Researchers developed NiCLIP, an AI model that decodes cognitive tasks, concepts, and domains from brain activation patterns. Trained on over 23,000 papers, NiCLIP excels at group-level decoding, ...

US researchers at the SLAC National Accelerator Laboratory have developed the Scalable Particle Imaging ...

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...

Using the power of prediction, University of Wisconsin–Madison mechanical engineers have quickly discovered several promising high-performance polymers out of a field of 8 million candidates. The ...

Literature searches, simulations, and practical experiments have been part of the materials science toolkit for decades, but the last few years have seen an explosion of machine learning-driven ...

The powerful chemotherapy drug cisplatin has been used since the late 1970s to treat a variety of cancers. It's highly effective against solid tumors and is often a core element of treatment for ...

Gene mutations in acute myeloid leukemia (AML) cells can guide treatment options, and machine learning can rapidly guess the existence of gene mutations based on images of leukemia cells. To solve ...

In the past two decades, the carbon-nitrogen bond forming reaction, known as the Buchwald-Hartwig reaction, has become one of the most widely used tools in organic synthesis, particularly in the ...

Researchers at City of Hope, and at the Translational Genomics Research Institute (TGen), have developed and tested a machine-learning approach that they suggest could one day enable earlier ...