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 …

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 …

The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An
initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore …

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 …

The optimal architecture of a deep feedforward neural network (DFNN) is essential for its
better accuracy and faster convergence. Also, the training of DFNN becomes tedious as the …

The multilayer perceptron (MLP) neural network is interpreted from the geometrical
viewpoint in this work, that is, an MLP partition an input feature space into multiple …

The performance of an artificial neural network (ANN) depends on the connection weights
and network structure. Many optimization algorithms have been applied for ANN model …

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

This research presents the implementation of machine learning (ML) for fault classification
and diagnosis on vehicle power transmission system (VPTS). Machine learning method can …

In this paper, we propose a new multi-objective evolutionary algorithm-based ensemble
optimizer coupled with neural network models for undertaking feature selection and …