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.

In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural Network is able to precisely predict and track the response of the actual system. The comparison between the results of this paper and those of the most recent published studies as NARX model indicates the significance of the proposed approach.

Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect water resources. Most of these models require input parameters that are hardly available or their measurements are time-consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by the human brain are a better choice. Materials and Methods: The present study simulated the groundwater level and salinity in Ramhormoz plain using ANN and ANN+PSO models and compared their results with the measured data. The data collected as inputs of the two models included minimum temperature, maximum temperature, average temperature, wind speed at 2 m altitude, minimum relative humidity, maximum relative humidity, average relative humidity, and sunshine hours gathered from 2011 to 2017. Results: The results indicated that the highest prediction accuracy of groundwater level and salinity was achieved by the ANN-PSO model with the logarithm sigmoid activation function. Thus, the MAE and RMSE statistics had the minimum and R^2 had the maximum value for the model. Conclusion: Considering the high efficiency of Artificial neural Network models with Particle Swarm Optimization algorithm training, it can be used to make managerial decisions, ensure the results of monitoring, and reduce costs.

Background and Aim: Discoloration is among the most common problems of composite restorations. Color change over time compromises the main advantage of composite resins namely their high esthetics. In such cases, the restoration needs to be replaced. .The aim of this in-vitro study was to evaluate the effect of accelerated Artificial aging (AAA) on the color stability of three composite resins (Filtek Z250, Filtek Z250XT, and Filtek Supreme). Materials and Methods: In this experimental study, 7 composite specimens with equal dimensions were fabricated of each composite resin. The initial color of specimens was measured using a spectroradiometer according to the CIE L*a*b* system. The specimens were then submitted to AAA for 384h and underwent color assessment again. Before and after aging, the surface roughness of one specimen from each group was determined by Atomic Force Microscopy (AFM). The obtained color parameters were analyzed by one-way ANOVA and Tukey’s test. Results: The color change of Filtek Z250 was significantly lower than that of Filtek Z250XT and Filtek Supreme (P≤0.05). No statistically significant differences were found between Z250XT and Supreme in this respect (P>0.05 ). Conclusion: All composite resins showed color change above the clinically acceptable threshold. Z250 microhybrid composite was more color stable than nano-composites (Z250XT and Supreme). AAA increased the surface roughness in all groups but it was within the clinically acceptable range.