Prediction of Combustion Efficiency using Multiple Neural Networks

Enhancing Efficiency of Neural Network Model in Prediction of Firms Financial Crisis Using Input Space Dimension Reduction Techniques

The main focus in this study is on data pre-processing, reduction in number of inputs or input space size reduction the purpose of which is the justified generalization of data set in smaller dimensions without losing the most significant data. In case the input space is large, the most important input variables can be identified from which insignificant variables are eliminated, or a variable ...

Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Prediction of Red Mud Bound-Soda Losses in Bayer Process Using Neural Networks

In the Bayer process, the reaction of silica in bauxite with caustic soda causes the loss of great amount of NaOH. In this research, the bound-soda losses in Bayer process solid residue (red mud) are predicted using intelligent techniques. This method, based on the application of regression and artificial neural networks (AAN), has been used to predict red mud bound-soda losses in Iran Alumina C...

Prediction of the Experimental Data for Removal of Organic Pesticides by Carbon Nanoparticle Synthesized from Pomegranate Peel using Artificial Neural Networks

Background and purpose: The present study is aimed to investigate the prediction of the experimental data for the removal of agricultural pesticides including three herbicides Trifluralin, Glyphosate, and 2,4-Dichlorophenoxyacetic acid from aqueous solution by carbon nanoparticles synthesized from pomegranate peel using artificial neural network. Materials and Methods: Removal studies were con...

Rock Brittleness Prediction Using Geomechanical Properties of Hamekasi Limestone: Regression and Artificial Neural Networks Analysis

The cold climate is a favorable parameter for the development of tension cracks and decrease of rock brittleness. Therefore, this paper attempts to investigate the Hamekasi porous limestone in order to predict the brittleness indices during freeze-thaw cycles. The freeze–thaw test was executed for one cycle including 16 h of freezing, and 8 h of thawing. The geo mechanical properties and brittl...

Prediction of Gas Lift Parameters Using Artificial Neural Networks