Background: Diabetes ever-increasing prevalence and the heavy burdens of controlling and treatment of the disease on people and the country have turned to be greatest challenges for governmental and healthcare authorities. Therefore, the disease prevention takes top priority and to do so the only possible way is detecting the effective parameters and controlling them. This study is about to foresee diabetes rates on the basis of some effective factors and using the artificial neural network.Methods: This study is conducted in 2014 by using R and SPSS software on 13423 participants of the study evaluation of risk factors of non-communicable diseases which was run in 2007. All the participants were older than 25 and with uncontrolled diabetes. A three-layer artificial neural network was used to evaluate the data, and to choose the best model the area under the ROC curve (AURC) and the prediction accuracy were applied. In this model both applied activation functions were Sigmoid.Results: The three-layer artificial neural network with the architecture of (53:20:2) was identified as the best model as the area under the ROC curve (AURC), the training prediction accuracy, and the test prediction accuracy were 72.7%, 92%, and 91.6% efficient, respectively.Conclusion: Since in artificial neural network there is no need for common assumption of classic statistical methods and its high prediction accuracy (53:20:2) it is highly recommended to apply this model in predicting diabetes. and factors affecting it, that requires a separate study and research.

Doubtlessly the first step in a river management is precipitation prediction of the watershed area. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) is extensively used as a non-linear inter-extrapolator by hydrologists. In the present study, Wavelet Analysis combined with artificial neural network and compared with Artificial Neural Network to predict the precipitation of Varayeneh station in the city of Nahavand. For this purpose, the original time series using wavelet theory decomposed to multi sub-signals. After this these sub-signals are used as input data to Artificial Neural Network to predict monthly Precipitation. The results showed that according to correlation coefficient of 0.92 and mean square error of 0.002 for the hybrid model of Wavelet- Artificial Neural Networks, the performance of this model is better than Artificial Neural Network with correlation coefficient of 0.75 and mean square error of 0.003 and can be used for short and long term precipitation prediction.

Artificial neural networks (ANNs) are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the capability of ANN in predicting body behavior in pain-producing situations is evaluated. A three-layer back-propagation ANN is designed using MATLAB software. The inputs include the magnitude of stimulation in pain fibers, touch fibers and central anti-nociceptive fibers and output is the level of perceived pain. In other words, we modeled the gate control theory of pain. Important features of pain process were chosen and defined in 8 features and then were applied to the ANN. We examined the ANN to ensure that it can model the real situations. The result was acceptable (errors below 1%). Our model can be used for interpolation and extrapolation of pain-related data. This model is a useful tool in pain experiments to predict the behavior of the organism.

Artificial Neural Networks (ANNs) with advantages such as learning and estimation capabilities are widely used in various fields of chemical engineering such as process simulation and control. They are suitable for modeling, simulation, and solution of highly nonlinear problems. One of these problems is Nonlinear Dynamic Data Reconciliation. In this paper a new method, namely NetDDR, which uses ANNs, is described. NDDR of a distillation column is used to illustrate different aspects and advantages of the new method.