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 …

For multilayer perceptron (MLP), the initial weights will significantly influence its
performance. Based on the enhanced fractional derivative extend from convex optimization …

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

In this paper a Bayesian regularized artificial neural network is proposed as a novel method
to forecast financial market behavior. Daily market prices and financial technical indicators …

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

Buildings are an essential part of our social life. People spend a substantial fraction of their
time and spend a high amount of energy in them. There is a grand variety of systems and …

Interval forecast is an efficient method to quantify the uncertainties in renewable energy
production. In this paper, the idea of prediction intervals (PIs) is employed to capture the …

The paper proposed an improved bacterial chemotaxis optimization (IBCO), which is then
integrated into the back propagation (BP) artificial neural network to develop an efficient …

Injection molding is the most widely used process in manufacturing plastic products. Since
the quality of injection molded plastic parts are mostly influenced by process conditions, how …

The real-time prediction of ship behavior plays an important role in navigation and intelligent
collision avoidance systems. This study developed an online real-time ship behavior …