the use of satellite data in an estimation of air temperature (ta) near the earth’s surface has turned into an effective way for a large area of high spatial and temporal resolution. throughout the present study, artificial neural network (ann) as well as m5 model tree were employed to estimate ta in khuzestan province (south west of iran), using satellite remotely sensed land surface temperatu...

BACKGROUND Artificial neural networks (ANN) are gaining prominence as a method of classification in a wide range of disciplines. In this study ANN is applied to data from a verbal autopsy study as a means of classifying cause of death. METHODS A simulated ANN was trained on a subset of verbal autopsy data, and the performance was tested on the remaining data. The performance of the ANN models...

background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime im­portance in designing and programming municipal solid waste management system. this study tests the short-term pre­diction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...

BACKGROUND This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. METHODS AND MATERIALS We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 in...

1-The manuscript definitely needs to rewrite by native speaker again. 2-The structure of the manuscript is little bit messy, because the discussion section has the "Introduction" materials 3-The conclusion section has very strong statement as a conclusion statement that says " The performans of the ANN model (the manuscript just compared 5 ANN models) with high value predicted CA dyfunction". B...

Prediction of software modules into fault-prone (FP) and not-fault-prone (NFP) categories using software metrics allows prioritization of testing resources to fault-prone modules for achieving higher reliability growth and cost effectiveness. This paper proposes an Artificial Neural Network (ANN) model with use of Sensitivity Analysis (SA-ANN) and Principal Component Analysis (PCA-ANN) for dime...

Data splitting is an important step in the artificial neural network (ANN) development process whereby data are divided into training, test and validation subsets to ensure good generalization ability of the model. Considering that only one split of data is typically used when developing ANN models, data splitting has a significant impact on the performance of the final model by potentially int...

Various models have been developed over the past several decades to predict the dynamic modulus /E*/ of hot-mix asphalt (HMA) based on regression analysis of laboratory measurements. The models most widely used in the asphalt community today are the Witczak 1999 and 2006 predictive models. Although the overall predictive accuracies for these existing models as reported by their developers are q...

The present work describes a comparison of the ability of multiple linear regression (MLR) and artificial neural networks (ANN) to predict fish spatial occupancy and abundance in a mesotrophic reservoir. Models were run and tested with 306 observations obtained by the sampling point abundance method using electrofishing. For each of the 306 samples, the relationships between physical parameters...

An artificial neural network (hence after, ANN) is an informationprocessing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. In previous two decades, ANN applications in economics and finance; for such tasks as pattern reorganization, and time series forecasting, have dramatically increased. Many central banks use forecasting models based ...