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 …

Artificial neural network (ANN) introduces different types of neural network structures and
has been applied successfully in diverse domains of real-world problems. Among various …

Abst The advent of Big Data has rendered Machine Learning tasks more intricate as they
frequently involve higher-dimensional data. Feature Selection (FS) methods can abate the …

Neural networks and fractional order calculus have shown to be powerful tools for system
identification. In this paper we combine both approaches to propose a fractional order neural …

Abstract System on chip (SoC) is a microchip which integrates many semiconductor devices
into a single chip. The complete system that is integrated with many components and circuits …

In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid
differential evolution KH (DEKH) method has been developed for function optimization. The …

Machine learning offers the potential to enable probabilistic-based approaches to
engineering design and risk mitigation. Application of such approaches in the field of blast …

The architecture of multilayer perceptron (MLP) neural networks dictates the network's
performance. However, aiming at the specific classification problems, suitable architectures …

Neural networks and genetic algorithms are the two sophisticated machine learning
techniques presently attracting attention from scientists, engineers, and statisticians, among …

The safety and stability of rotors are significantly important for smooth operations of steam
turbines. To predict the fatigue life of a 350 MW supercritical steam turbine rotor online, a …