Artificial neural networks had their first heyday in molecular informatics and drug discovery
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …

This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a
variety of emerging services and applications. However, the presence of rogue IoT devices …

We present a new framework for analyzing and learning artificial neural networks. Our
approach simultaneously and adaptively learns both the structure of the network as well as …

Beyond energy, the growing number of defects in physical substrates is becoming another
major constraint that affects the design of computing devices and systems. As the underlying …

Despite state-of-the-art solutions to detect phishing attacks, there is still a lack of accuracy for
the detection systems in the online mode which is leading to loopholes in web-based …

This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-
RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure …

Predicting urban air quality is a significant aspect of preventing urban air pollution and
improving the living environment of urban residents. The air quality index (AQI) is a …

The success of an artificial neural network (ANN) strongly depends on the variety of the
connection weights and the network structure. Among many methods used in the literature to …

We present an approach to feature extraction that is a generalization of the classical linear
discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA …