Abstract Machine learning models have been shown to be useful for predicting and
assessing structural performance, identifying structural condition and informing preemptive …

Quantum computing has rapidly advanced in recent years due to substantial development in
both hardware and algorithms. These advances are carrying quantum computers closer to …

Document-level sentiment classification aims to automate the task of classifying a textual
review, which is given on a single topic, as expressing a positive or negative sentiment. In …

Semi-supervised learning is a learning paradigm concerned with the study of how
computers and natural systems such as humans learn in the presence of both labeled and …

The intent of this book is to describe some recent data mining tools that have proven
effective in dealing with data sets which often involve unc-tain description or other …

Predicting mechanical properties of graphene-reinforced metal matrix nanocomposites
(GRMMNCs) usually requires atomistic simulations that are computationally expensive …

This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …

Non-dimensional similarity groups and analytically solvable proximity equations can be
used to estimate integral fluid film parameters of elastohydrodynamically lubricated (EHL) …

This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …

According to global statistics and the world health organization (WHO), about 17.5 million
people die each year from cardiovascular disease. In this paper, the heart sounds gathered …