in this work the electrooxidation half-wave potentials of some benzoxazines were predicted from their structural molecular descriptors by using quantitative structure-property relationship (qsar) approaches. the dataset consist the half-wave potential of 40 benzoxazine derivatives which were obtained by dc-polarography. descriptors which were selected by stepwise multiple selection procedure ar...

The paper describes an artificial neural network (ANN) model to predict the height of destressed zone (HDZ) which is taken as equivalent to the combined height of caved and fractured zones above the mined panel in longwall mining. For this, the suitable datasets have been collected from the literatures and prepared for modeling. The data were used to construct a multilayer perceptron (MLP) netw...

Green energy sources are implemented for the generation of power due to their substantial advantages. Wind is best among renewable options generation. Generally, wind system directly connected with network supplying power. In direct connection, there an issue managing quality (PQ) concerns such as voltage sag, swells, flickers, harmonics, etc. order enhance PQ in a conversion (WECS), peripheral...

In the present investigation, an artificial neural network (ANN) model is developed to predict the isothermal hot forging behavior of AlCuMgPb aluminum alloy. The inputs of the ANN are deformation temperature, frictional factor, ram velocity and displacement whereas the forging force, barreling parameter and final shape are considered as the output variable. The developed feed-forward back-prop...

Considering the importance of Cd and U as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the Eshtehard region in Iran by means of a developed artificial neural network (ANN) model. The forward selection (FS) method is used to select the input variables and develop hybrid models by ANN. From 45 input candidates, 13 and 14 ...

In this work, artificial neural network (ANN) has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocar...

abstract process of evapotranspiration (eto) is a major component of the hydrologic cycle that its accurate estimation plays an important role to achieve sustainable development in water balance, irrigation system design and planning and management of water resources. being a function of different metrological parameters and their interactions, evapotranspiration is a complex, nonlinear phenome...

applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. artificial neural networks (ann) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. most ann models applied in economics use the gradient descent method as their learning algorithm. however, t...

Background: Diabetes and hypertension are important non-communicable diseases and their prevalence is important for health authorities. The aim of this study was to determine the predictive precision of the bivariate Logistic Regression (LR) and Artificial Neutral Network (ANN) in concurrent diagnosis of diabetes and hypertension. Methods: This cross-sectional study was performed with 12000 ...