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 …

Currently, workers in nuclear power plants need to monitor plant data in real time. In the
event of an emergency, due to human subjectivity, the operator cannot make accurate …

Recently, artificial intelligence technologies have received considerable attention because
of their practical applications in various fields. The key factor in this prosperity is deep …

In this study, a novel stochastic computational frameworks based on fractional Meyer
wavelet artificial neural network (FMW-ANN) is designed for nonlinear-singular fractional …

This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation
neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP …

This study presents an accident causation model for repair and maintenance related
accidents at oil refineries and proposes the best model for early accident prediction through …

Manufactured sand is increasingly used in concrete and predicting the rheology of such
suspensions based on their composition are necessary. In this study, emphasis is on cement …

Nuclear reactor dynamics has a non-linear nature, and some parameters depend on the
output power level. Accordingly, a load-following issue is important. To this end, it is …

Deep neural networks, which have proven to be effective methods to solve complex
problems, can even be applied in decision systems controlling critical processes. However …

Nowadays, pipelines have been extensively used for transporting oil and gas for long
distances. Therefore, their risk assessment could help to identify the associated hazards and …