A data-driven approach can retire the endless, unproductive battles over race- and class-based affirmative action.

In the ever-growing field of machine learning, one of the most significant challenges is making complex models interpretable and accessible. Enter AutoXplainAI, an innovative framework developed by ...

It provides a wide variety of machine learning algorithms designed to be scalable and capable of running on large datasets using distributed computing frameworks like Apache Hadoop and Apache Spark.

Department of EECS, University of California at Berkeley, 485 Soda Hall, Berkeley CA 94720-1776, USA Previously at: Institute for Adaptive and Neural Computation, University of Edinburgh, UK.

It can be relatively cheap to gather a lot of bio-signal data. To teach a machine-learning algorithm to find a relationship between bio-signals and health outcomes, however, you need to teach the ...

In 2017, Ezekiel Emanuel, a well-known oncologist and health policy commentator, said radiologists would soon be out of work thanks to machine learning. That hasn’t happened, but although ...

A machine-learning algorithm, catGRANULE 2.0 ROBOT, has been developed to predict the potential of proteins to form toxic aggregates linked to neurodegenerative diseases like ALS, Parkinson's, and ...