The artificial neural networks (ANN) are the learning algorithms and mathematical models, which mimic the information processing ability of human brain and can be used to non linear and complex data. The aim of this study was to compare artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. The data of 1389 goats for body weight, height at withers ...

OBJECTIVE HIV treatment failure is commonly associated with drug resistance and the selection of a new regimen is often guided by genotypic resistance testing. The interpretation of complex genotypic data poses a major challenge. We have developed artificial neural network (ANN) models that predict virological response to therapy from HIV genotype and other clinical information. Here we compare...

This study aimed to investigate and compare nonlinear growth models (NLMs) with the predicted performance of broilers using an artificial neural network (ANN). Six hundred forty broiler chicks were sexed and randomly reared in 32 separate pens as a factorial experiment with 4 treatments and 4 replicates including 20 birds per pen in a 42-day period. Treatments consisted of 2 metabolic energy le...

In this paper an intelligent separation system, based on artificial neural networks (ANNs), for classifying four different varieties of Iranian pistachio nuts, namely, Kaleghouchi (Ka), Akbari (Ak), Badami (Ba) and Ahmadagaee (Ah) is presented. To develop the ANN models a total of 3200 pistachio sound signals, 800 samples for each variety, were recorded. Features of pistachio nut varieties were...

determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. in this paper, the application of artificial intelligence (ai) methods to data analysis,namely artificial neural network (ann), hybrid ann with biogeography-based optimization (ann-bbo), and multi-output adaptive neural fuzzy inference system (manfis) to estima...

In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonli...

Building a numerical model in not an easy task. In addition to its need for verification, it requires special skills and experiences in mathematics, computer programming and may take long period to be ready for operation and prediction. On the other hand, artificial neural network ANN) prediction models are more efficient in predictions once they are trained from examples or patterns. These typ...

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. ...

Using a large sample of 46,467 residential properties spanning 1999-2005, we demonstrate using matched pairs that, relative to linear hedonic pricing models, artificial neural networks (ANN) generate significantly lower dollar pricing errors, have greater pricing precision out-of-sample, and extrapolate better from more volatile pricing environments. While a single layer ANN is functionally equ...