The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …

Decision-making on numerous aspects of our daily lives is being outsourced to machine-
learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in …

Remote sensing approaches to measuring inland water quality date back nearly 50 years to
the beginning of the satellite era. Over this time span, hundreds of peer-reviewed …

Abstract oTree is an open-source and online software for implementing interactive
experiments in the laboratory, online, the field or combinations thereof. oTree does not …

Data preprocessing is an often neglected but major step in the data mining process. The
data collection is usually a process loosely controlled, resulting in out of range values, eg …

Many sciences have made significant breakthroughs by adopting online tools that help
organize, structure and mine information that is too detailed to be printed in journals. In this …

Data Mining (DM) is the process for automatic discovery of high level knowledge by
obtaining information from real world, large and complex data sets [26], and is the core step …