Gearbox is a main component of rotating machines. Any fault in gearboxes not detected and removed on time, results in stopping the system or damaging it. So, early detection of faults is of critical importance. Here, in order to automatic detection of gear faults based on Acoustic emission signals Logistic Model Tree (LMT) classification method is applied. Wavelet transform is used to analyse Acoustic Emission signals recorded from a defected gear. Then, some discriminative features are extracted from different frequency bands. From many different extracted features, the most relevant ones are selected using feature selection algorithms. At last, a Logistic Model Tree is used to classify the undefected and defected gears; also, the types of defects are recognized. Undefected and defected gears are discriminated with 99% of accuracy and types of defects are correctly identified with the rate of 80%.

In this paper, we propose a mathematical model for the human arterial tree. The finite element method is used to solve the set of one-dimensional governing equations for blood flow. This model is able to predict the characteristics of blood flow such as the pressure wave propagation and the flow rate with reasonable accuracy. Comparison between three different models of blood; Newtonian, Power Law and Casson shows that the Casson model has almost similar results as the Newtonian, and the results from the Power Law model are close to the inviscid model. Comparison between the steady-state and the unsteady model demonstrates that the steady-state model can be used to predict the mean value of the unsteady variations within an acceptable accuracy range. The proposed model is also capable to predict the effects of different parameters on the blood pressure and the flow rate in arterial tree such as resistance and compliance of distal capillary beds.

Consideration of water quality and implementation of appropriate actions for preventing of water resources pollution is a very important issue in Iran because of surface water deficit. Sustainable development of agriculture is impossible without considering of surface water quality. Water quality control is a noteworthy issue in irrigation scheduling program of agricultural land. Since surface water quality monitoring and assessment is very expensive and time consuming. Thus, finding a cheap, simple and relatively exact method which can predict the water quality class base on minimum hydro chemical parameters would be very useful. Decision tree as one of the data mining techniques classify data sets based on a tree structure and uses for prediction base on extracting the exiting patterns and roles among data sets. In this study, the decision tree method was used to classify water quality in some hydrometrics stations located at southern side of Sahand Mountain, including Chekan, Girmizigol, Shishovan, Tazekand and Moghanjig. The water quality classes were defined based on if-then rules. The results showed that the decision tree method is able to predict the water quality classes based on small number of hydro chemical parameters with high accuracy.

Introduction: In the last 10 years The incidence of diabetes has doubled worldwide with annual increasing rate of about 6%. More than 2 million people in Iran are now affected by this disease.The present research deals with the relation between the observed complications of type 2 diabetic patients and some related features like Blood Glucose Level, Blood Pressure, Age, and Family History. The main purpose was to predict the patients’ complications based on the observed signs.Methods: The research data were gathered from 856 patient records related to the 2009’s cases in the Diabetes Center of Golestan province. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called C5.0 decision tree and Neural Network were used. Celementine 12.0 software was implemented For data analysis.Results: The results of data mining showed that the variables of high blood pressure, age, and family history had the most impact on the observed complications. Based on the created decision tree, some rules have been extracted which can be used as a pattern to predict the probability of occurring these complications in the patients. The accuracy of the C5.0 model on the data was shown to be 89.74% and on the Artificial Neural Network was 51.28%.Conclusion: As the highest accuracy was shown to be achieved using C5.0 algorithm, according to the created rules, it can be predicted which complication (s) any diabetic patient with new specified features may probably suffer from.

The purpose of recent study is recognition of the most important financial ratios by which can evaluate company's performance. Therefore the total of accepted companies in stock exchange in Tehran which was active in 1390-1393 are considered as statistical universe of the research through which 102 companies organize the mass of statistical sample based on systematic elimination sampling method. The from the view point of exploratory and functional purpose, the research method is descriptive and interconnection including post – eventual researches. Analysis of data is accomplished by factorial analysis, structural equations modeling and two algorithms by using of CHAID, C&RT software, SPSS, SMARTTPLS, CLEMENTIN decision tree. after explanatory, factorial analysis, the results of research show that the number of 24 ratios from the total of considered 28 financial ratios is effective in evaluation of company's performance which these ratios are classified in seven categories in terms of weight of each of them from total variance by using of main factor analysis PCA. in next stage, for studying the type of relations and the amount of variant interconnection, confirmatory factorial analysis is performed in structural equations modeling and the main model presented. Finally the result and drawing decision tree indicate that decision tree algorithms are presented the best prediction with the highest accuracy and among the sum of ratios, activity ratio has the most effect on performance evaluation.

The evapotranspiration of the reference Crop is of particular importance due to the changes in climate parameters of temperature, sunlight, humidity and wind speed in combination. the purpose of this study is to investigate the effects of climate change on evapotranspiration of apple during the growing season.For this purpose, the ECMWF database has been used for observation data of Semirom and Urmia stations during 20-year period (1996-2001).To check this quantity in the next 20 years, the daily  Downscaling dynamic data of the CORDEX project with a precision of 44% * 44% for the output of the ICHEC-EC-EARTH model under the two lines of 4.5 and 8.5 (RCP) was used for the period (2017-2037). In order to reduce the errors in the model estimates, the post-processing action of the estimated events was fulfilled. Then, minimum temperature data, maximum temperature, relative humidity, wind speed and radiation, potential evapotranspiration have been calculated using Penman- Monteith FAO method, which is more accurate than other models, and using the non-parametric Man-Kendall test and the Sen’s Slope estimator nonparametric Method in the confidence range, 95% evapotranspiration was determined. The results showed that evapotranspiration in both stations is increasing during the growing season. The ETo increase in the growth season of the apple tree stations was predicted from the base period for the trajectory of 4.5 and 8.5 for the Semirom 4.14.7 and 7.99.7, respectively, and for Orumiye Station, 26.5 and 11.8, respectively