background : in medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. due to the limitations of usual statistical models, other methods such as artificial neural network (ann) and hybrid models could be used. in this paper, we propose a hybrid artificial neural network-genetic algorithm (ann-ga) model to predictio...

artificial neural networks (ann) have shown to be a powerful tool for system modeling in a wide range of applications. the focus of this study is on neural network applications to data analysis in egg production. an ann model with two hidden layers, trained with a back propagation algorithm, successfully learned the relationship between the input (age of hen) and output (egg production) variabl...

in this work, artificial neural network (ann) has been employed to propose a practical model forpredicting the surface tension of multi-component mixtures. in order to develop a reliable modelbased on the ann, a comprehensive experimental data set including 15 ternary liquid mixtures atdifferent temperatures was employed. these systems consist of 777 data points generally containinghydrocarbon ...

simulation of groundwater fluctuations plays a crucial role in management of watersheds and water demand balancing. recently, wavelet analysis has been used widely in time series decomposition and coupling with neural networks for hydrological modeling. in this paper, the ability of the wavelet-dynamic artificial neural networks (w-ann) model was applied in forecasting one-month-ahead of ground...

background: as bremsstrahlung photon beam fluence is important parameter to be known in a photonuclear reaction experiment as the number of produced particle is strongly depends on photon fluence. materials and methods: photon production yield from different thickness of aluminum target has been estimated using artificial neural network (ann) model. target thickness and incoming electron energy...

in this paper, a new approach of modeling for artificial neural networks (ann) models based on the concepts of ann and fuzzy regression is proposed. for this purpose, we reformulated ann model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ann models. hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility. in ...

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 ira...

in today’s world renewable energy is highly demanding because of increasing oil and fuelprices, desire and necessity to avert irreversible climate damage and also the unreliability of the non -renewablesources. in renewable energy, water is an important source. previously water is used only for survivalof human being as much as food but no attentionhas been givenen to economical use and conserv...