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 …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

This paper presents a deep neural network (DNN) approach for induction motor fault
diagnosis. The approach utilizes sparse auto-encoder (SAE) to learn features, which …

Flash floods are responsible for loss of life and considerable property damage in many
countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone …

This paper introduces a new deep-learning algorithm of deep belief network (DBN) based
on an extreme learning machine (ELM) that is structured by back propagation (BN) and …

Deep evolutionary network structured representation (DENSER) is a novel evolutionary
approach for the automatic generation of deep neural networks (DNNs) which combines the …

Liquidity risk represent a devastating financial threat to banks and may lead to irrecoverable
consequences in case of underestimation or negligence. The optimal control of a …

In this paper, a prediction model for hot-rolled strip crown based on an artificial neural
network (ANN) is presented. Considering the mean squared error (MSE) and correlation …

A deep learning (DL) model is inherently imprecise. To address this problem, existing
techniques retrain a DL model over a larger training dataset or with the help of fault injected …

Cancer is one of the leading causes of morbidity and mortality worldwide with increasing
prevalence. Breast cancer is the most common type among women, and its early diagnosis …