In most traditional machine learning algorithms, the training and testing datasets have
identical distributions and feature spaces. However, these assumptions have not held in …

A typical microarray dataset usually contains thousands of genes, but only a small number of
samples. It is in fact that most genes in a DNA microarray dataset are not relevant for …

Over the last few decades, algorithms inspired by nature have matured into a widely used
class of computing methods. They have shown the ability to adjust to variety of conditions …

This paper proposes a multi-objective phasor measurement units (PMUs) placement method
in electric transmission grids. Further consideration is devoted to the early PMU placement …

Dyslexia is a learning disorder in which individuals have significant reading difficulties.
Previous studies found that using machine learning techniques in content supplements is …

User satisfaction as a significant antecedent to user loyalty has been highlighted by many
researchers in market based literatures. SLA violation as an important factor can decrease …

Software-defined networking is a modern and popular network technology that utilizes
controllers as network operating systems to manage the network and allow user applications …

This book is written for computer engineers, scientists, and students studying/working in
reinforcement learning and artificial intelligence domains. The book collects recent …

The cellular learning automaton (CLA), which is a combination of cellular automaton (CA)
and learning automaton (LA), is introduced recently. This model is superior to CA because of …

Scientific modelling is a prime means to generate understanding and provide much‐needed
information to support public decision‐making in the fluid area of sustainability. A growing …