One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …

Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …

Abstract Machine learning has become a pivotal tool for many projects in computational
biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical …

Data science models, although successful in a number of commercial domains, have had
limited applicability in scientific problems involving complex physical phenomena. Theory …

Background Many mortality prediction models have been developed for patients in intensive
care units (ICUs); most are based on data available at ICU admission. We investigated …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

In this article, we introduce the BART R package which is an acronym for Bayesian additive
regression trees. BART is a Bayesian nonparametric, machine learning, ensemble …

Deep learning provides exciting solutions in many fields, such as image analysis, natural
language processing, and expert system, and is seen as a key method for various future …

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the
coding regions are frequently associated with the development of various genetic diseases …