Background and Objectives: Efficient cost management in hospitals’ pharmaceutical inventories have the potential to remarkably contribute to optimization of overall hospital expenditures. To this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. While the linear methods are frequently used for forecasting purposes chiefly due to their si...

drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. identification of resistant phenotype is very important because it can lead to effective treatment plan. there is an interest in developing classifying models of resistance phenotype based on the multivariate data. we have investigated a vibrational spectroscopic approach in order to characterize a sen...

In this work quantitative structure activity relationship (QSAR) methodology was applied for modeling and prediction of cellular response to polymers that have been designed for tissue engineering. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regressions (MLR) and artificial neural network (ANN) methods. The root m...

one of the main aims of water resource planners and managers is to estimate and predict the parameters of groundwater quality so that they can make managerial decisions. in this regard, there have many models developed, proposing better management in order to maintain water quality. most of these models require input parameters that are either hardly available or time-consuming and expensive to...

the quantitative structure-retention relationship (qsrr) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. the genetic algorithm (ga) was employed to select the variables that resulted in the best-fitted models. after the variables were selected, the linear multi...

over the last decade or so, artificial neural networks (anns) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. however, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. for this reason, this study aims at de...

The purpose of this study is to assess the ability of the artificial neural network (ANN) models in estimating river ice thickness using easy available climate data. A site specific ANN models were developed for two hydrometric stations at two rivers in Alberta (Canada). The ANN models were found to adequately estimate ice thickness. Ways to improve the performances of the ANN models are proposed.

Precise knowledge ofthe amount of global solar radiation plays an important role in designing solar energy systems. In this study, by using 22-year meteorologicaldata, 19 empirical models were tested for prediction of the monthly mean daily global solar radiation in Tabriz. In addition, various Artificial Neural Network (ANN) models were designed for comparison with empirical models. For this p...

Background: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and health administrators. The current study aimed at developing and comparing some statistical models to identify the risk factors associated with type 2 diabetes. In this light, artificial neural network (ANN), support vector machines (SVMs), and multi...