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.

Background: Abortion is an important and controversial issue and one of the important reasons for the mortality of pregnant women worldwide. This study aimed to predict the risk factors of abortion in pregnant women using Artificial Neural Network, wavelet Neural Network, and adaptive Neural fuzzy inference system. Materials and Methods: The study is an analytical-comparative modeling and data of 4437 pregnant women from the Ravansar Non-Communicable Disease (RaNCD) cohort study from 2014 to 2016 was used. First, six variables were chosen through the genetic algorithm approach, then Artificial Neural Network (ANN), wavelet Neural Network (WNN), and adaptive Neural fuzzy inference system (ANFIS) were run. Finally, the performance of the models was compared based on the evaluation criteria. All analyses were done in MATLAB R2019b software. Results: ANN with RMSE of 0. 019 showed better performance than ANFIS and WNN with 0. 42 and 1. 445, respectively. Further, the accuracy, sensitivity, and specificity in ANN were 100%, 99%, and 100%, while in WNN, they were 76. 2%, 76. 4%, and 66. 7%. However, when the researchers used three selected variables, the accuracy, sensitivity, and specificity as well as RMSE in ANFIS were 100%, 100% 100%, and 0,100%, 99%, 100%, and 0. 021 in ANN,and finally 76. 2%, 76. 4%, 38. 5%, and 1. 553 in WNN. Conclusion: The models with six input variables indicated that the Artificial Neural Network has a better performance than the other two models, but based on the three variables, the fuzzy Neural inference system performed better than the other two models.

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.

Estimation of evapotranspiration is essential for planning, designing and managing irrigation and drainage schemes, as well as water resources management. In this research, Artificial Neural Networks, Neural Network wavelet model, multivariate regression and Hargreaves' empirical method were used to estimate reference evapotranspiration in order to determine the best model in terms of efficiency with respect to the existing data. The daily data of two meteorological stations of Shahrekord and Farrokhshahr airport in the dry and cold zones of Shahrekord during the period 2013-2004 was used; these included the minimum and maximum temperature, the average nominal humidity, wind speed at 2 meters height and sunshine hours. %75 of the data were validated, and %25 of the data was used for testing the models. Designed Network is a predictive Neural Network with an active sigmoid tangent function hidden in the layer. In the next step, different wavelets including Haar, db and Sym were applied on the data and the Neural Network-wavelet was designed. To evaluate the models, the method was used by the Penman-Montith Fao and for all four methods, RMSE, MAE and R statistical indices were calculated and ranked. The results showed that the wave-let-Neural Network with the db5 wavelet had a better performance than other wavelets, as well as the Artificial Neural Network, multivariate regression and the Hargreaves method. The results of wavelet Network modelling with the db5 wavelet in the Farrokhshahr station were calculated to be 0. 2668, 0. 2067 and 0. 998, respectively; at the airport station, these were equal to 0. 2138, 0. 14 and 0. 9989, respectively. The results, therefore, showed that the Neural Network-wavelet performance was more accurate than the other models studied in this study.

Inverters have been widely used in renewable energy as a means of converting extracted power to grid standards. However, this power electronics equipment is highly vulnerable to failures due to its complex architecture and components. One of the main sources of failure is semiconductor switches that are critically sensitive to abnormal conditions such as high voltage. With the advent of multilevel inverters, this concern has been raised considerably due to the increase in the number of switches. This paper has proposed a novel method with a Neural Network that can detect open circuit failure of switches and replace them with some new arrangement in the inverter so that it can run effectively. Simulations with MATLAB/Simulink for a seven level inverter, illustrate that with a switch failure, the multilevel inverter can work successfully. Results also demonstrate that this method is fast and can compensate for the output in less than two cycles. Therefore, it can be used in reliable multilevel applications in which the power flow should be achieved even if a semiconductor switch is broken.