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 …

Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

The back propagation neural network (BPNN) can easily fall into the local minimum point in
time series forecasting. A hybrid approach that combines the adaptive differential evolution …

Direct measurements of formation properties such as the shale volume, porosity,
permeability, and fluid saturation are often accompanied by expensive cost and are time …

This paper proposes an appropriate combinational approach which is based on improved
BP neural network for short-term gas load forecasting, and the network is optimized by the …

Reliable energy consumption forecasting can provide effective decision-making support for
planning development strategies to energy enterprises and for establishing national energy …

The paper explores the usefulness of hybridizing two distinct nature inspired computational
intelligence techniques viz., Artificial Neural Networks (ANN) and Genetic Algorithms (GA) …

In the field of marine detection and warning, predicting the heights of ocean wave is a very
important project. In order to predict the ocean wave heights accurately and quickly, our …

The most frequent and noticeable natural calamity in the Karakoram region is landslides.
Extreme landslides have occurred frequently along Karakoram Highway, particularly during …

Assessment of bond behavior of glass fiber-reinforced polymer (GFRP) bars to concrete
plays an important role in design and implementation of the polymer-matrix composites …