The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

Artificial neural networks, or neural networks, represent a technology that is rooted in many
disciplines like mathematics, physics, statistics, computer science and engineering. Neural …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

We present a comprehensive review of the evolutionary design of neural network
architectures. This work is motivated by the fact that the success of an Artificial Neural …

The most important problems with exploiting artificial neural networks (ANNs) are to design
the network topology, which usually requires an excessive amount of expert's effort, and to …

Since neural networks have universal approximation capabilities, therefore it is possible to
postulate them as solutions for given differential equations that define unsupervised errors …

A significant goal of modern universities is to provide high-quality education to their students
and reduce their failure rates. The early recognition of low-performance students that would …

Optimizing the hyper-parameters of a multi-layer perceptron (MLP) artificial neural network
(ANN) is not a trivial task, and even today the trial-and-error approach is widely used. Many …

In recent decades, artificial neural networks (ANNs) have been extensively applied in
different areas such as engineering, medicine, business, education, manufacturing and so …