Feature selection (FS) is a machine learning process commonly used to reduce the high
dimensionality problems of datasets. This task permits to extract the most representative …

In this study, we propose an improved version of the adaptive neuro-fuzzy inference system
(ANFIS) for forecasting the air quality index in Wuhan City, China. We propose a hybrid …

Training artificial neural networks is considered as one of the most challenging machine
learning problems. This is mainly due to the presence of a large number of solutions and …

Power system security assessment and enhancement are two major crucial issues in a large
interconnected power system. System security can be classified on the basis of major …

The appropriate planning of electric power systems has a significant effect on the economic
situation of countries. For the protection and reliability of the power system, the optimal …

Recently, pre-trained deep learning (DL) models have been employed to tackle and
enhance the performance on many tasks such as skin cancer detection instead of training …

Multilevel thresholding is one of the most popular approaches used for image segmentation.
Several methods have been used to find the threshold values; however, metaheuristic (MH) …

In engineering, it is computationally prohibitive to directly employ costly models in
optimization. Therefore, surrogate-based optimization is developed to replace the accurate …

Radial basis function neural networks (RBFNNs) are widely used in nonlinear function
approximation. One of the challenges in RBFNN modeling is determining how to effectively …

Neural-symbolic systems are based on both logic programming and artificial neural
networks. A neural network is a black box that clearly learns the internal relations of …