Artificial neural networks are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the efficiency of a neural network for predicting the survival of patients with acute pancreatitis is compared with days-of-survival obtained from patients. A three- layer back-propagation neural network was developed for this purpose. Clinical data (e.g...

Objective (s): Artificial Neural Networks (ANN) are widely used for predicting systems’ behavior. GMDH is a type of ANNs which has remarkable ability in pattern recognition. The aim the current study is proposing a model to predict dynamic viscosity of silver/water nanofluid which can be used as antimicrobial fluid in several medical purposes.Materials and Methods: In order to have precise mode...

abstract infiltration process is one of the most important components of the hydrological cycle. on the other hand, the direct measurement of infiltration process is laborious, time consuming and expensive. in this study, the possibility of predicting cumulative infiltration in specific time intervals, using readily available soil data and artificial neural networks (anns) was investigated. for...

abstract autoregressive integrated moving average (arima) has been one of the widely used linear models in time series forecasting during the past three decades. recent studies revealed the superiority of artificial neural network (ann) over traditional linear models in forecasting. but neither arima nor anns can be adequate in modeling and forecasting time series since the first model cannot d...

abstract-manual harvesting of saffron as a laborious and exhausting job; it not only raises production costs, but also reduces the quality due to contaminations. saffron quality could be enhanced if automated harvesting is substituted. as the main step towards designing a saffron harvester robot, an appropriate algorithm was developed in this study based on image processing techniques to recogn...

as is well known, estimating parameters of the tree-parameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. weibull distribution involves in reliability studies frequently and has many applications in engineering. however estimating the parameters of weibull distribution is crucial in classical ways. this distribution has t...

this paper investigates the performances of artificial neural networks approximation, the translog and the fourier flexible functional forms for the cost function, when different production technologies are used. using simulated data bases, the author provides a comparison in terms of capability to reproduce input demands and in terms of the corresponding input elasticities of substitution esti...

This paper reports on the use of Artificial Neural Networks (ANN) and Partial Least Squareregression (PLS) combined with NIR spectroscopy (900-1700 nm) to design calibration models for thedetermination of sugar content in sugar beet. In this study a total of 80 samples were used as the calibration set,whereas 40 samples were used for prediction. Three pre-processing methods, including Multiplic...

In the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks a...