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.

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.

The growing elderly population will bring serious problems to society. Depression is one of the major disorders of old age that can be affected by various factors such as gender, age, education, and place of residence, among others. However, most of these variables are not fully controllable and they can interact with each other. Therefore, it is often difficult to find relationships between these variables using regression models that have restrictive assumptions. In this study, the artificial neural network (ANN) models were used to overcome this dilemma. We determined the effect of variables of age, marital status, number of family members, income, employment status, homebound status, gender, place of residence (city or village), the number of chronic non-communicable diseases, and ethnicity on depression in the elderly. Data were analyzed using SPSS22 software for 1, 477 people aged 60-92 years. The bestANNmodel had 33 neurons in the hidden layer and a sigmoid transfer function in both hidden and output layers. The preferred ANN model had a minimum sensitivity of 60% to determine the level of depression in the elderly. This model introduced ethnicity, the number of households, and the number of chronic diseases, age, and income as the most effective variables in predicting depression.

Structural vibration control is one of the most important features in structural engineering. Real-time information about seismic resultant forces is required for deciding module of intelligent control systems. Evaluation of lateral forces during an earthquake is a complicated problem considering uncertainties of gravity loads amount and distribution and earthquake characteristics. An artificial neural network (ANN) has been trained in this article to estimate these forces. This ANN was trained on the results of time history analysis of a three-story building under 702 different loadings. Results of numerical examples verify that the trained ANN can predict the expected forces with negligible deviations.

Objective: In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated.Materials and Methods: In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used to train ANN.Results: The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R2) between the actual and predicted values was determined as 0.897118 for all data.Conclusion: It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week).