Recognition equal units and segregation them and upshot planning per units most basic method for management Forest units. Aim this study presentation and comparison classification and regression Tree (CART) and Random Forest (RF) algorithm for Forest type mapping using ASTER satellite data in district one didactic and research Forest's darabkola. In start using inventory network 500* 350 m, take number 150 sample plat in over district. After accomplish Geometric correction and reduce atmospheric effect on image processing bands rationing, create general vegetation indices, principal component analysis and tesslatcap index. After extraction spectrum values relevant by sample plats fabric and processing bands, classification values other pixel accomplish using investigating algorithms. Evaluation accuracy results classification accomplish by some sample plat that not participate in process classification. The result showed preparation map using RF with overall accuracy 66% and kappa coefficient 0.57 than classification and regression Tree with overall accuracy 58% and kappa coefficient 0.49 has superior accuracy. Totality result showed using above algorithm may increased accuracy Forest type map.

Background: Diabetes is the fourth leading cause of death in the world. And because so many people around the world have the disease, or are at risk for it, diabetes can be called the disease of the century. Diabetes has devastating effects on the health of people in the community and if diagnosed late, it can cause irreparable damage to vision, kidneys, heart, arteries and so on. Therefore, it is necessary to have methods to diagnose this disease in the early stages. In this article, data mining is used to diagnose diabetes. Methods: The main algorithm used in this paper is the Random Forest algorithm. To evaluate the efficiency of the proposed algorithm in diagnosing diabetes, a data set was used that included 768 samples (patients) and had 8 characteristics. Because the stochastic Forest algorithm is a hybrid algorithm created from several decision Trees, it achieves high accuracy in diagnosing diabetes. Results: Using this algorithm, we were able to increase the accuracy of diabetes diagnosis to 99. 86%. Conclusion: Diabetes is the fourth leading cause of death in the world. Different algorithms have been used to diagnose this disease. We tried to use an algorithm that has a very high degree of accuracy compared to other algorithms for diagnosing this disease.

Today, the risk-based audit method is emphasized in modern tax systems, so explaining a comprehensive model for rating the risk of taxpayers is one of the basic steps of implementing a comprehensive tax plan. Therefore, in this article, we aim to measure the performance of decision Tree algorithms and support vector machine in the validation of taxpayers. The statistical population of this research is the companies accepted in the Tehran Stock Exchange, which were active during the years 2012-2017 and for the selection of the sample was made using the screening method (elimination). In this research, first, using Delphi technique and meta synthesis, 164 effective components in the validation of taxpayers were identified, then the data needed to measure the variables of the research were extracted from the Kodal website and by examining tax files, and finally by using the collected data, we investigated the accuracy of the decision Tree (C5. 0 algorithm and Random Forest) and support vector machine in validating taxpayers. The findings showed that based on the results of the AUC value, the C5. 0 algorithm and the Random Forest have a better fit, however, the research hypothesis that it is possible to predict the risk of taxpayers using the SVM algorithm is not rejected.

This study investigates the ability of tax evasion prediction of listed companies in Tehran Stock Exchange (TSE) by Decision Tree algorithms. Statistical population of this study is all companies listed in TSE from 2005 to 2016. Statistical sample includes 1081 year-company. Data was analyzed by One-Way ANOVA and Decision Tree algorithms. In this regard, research data test was done by using SPSS and Weka softwares. The research results showed that the best performances are respectively as what follows here: Random Forest, REPTree, J48, LMT, Decision Stump, and Random Tree. In addition, the One-Way ANOVA showed that differences in the efficiency of Decision Tree algorithms are statistically significant.

In this study, the site form index which is the most reliable criterion for evaluation of Forest site productivity in uneven-aged and mixed stands was used. For this purpose, Random-systematic sampling method was used to locate 105 0.1 ha circular sample plots in beech dominated Forests in Tarbiat Modares University research Forest. The height and diameter ofFagus orientalis Lipsky Trees within each sample plot was recorded along with elevation, azimuth and slope of the ground. Also, at the center of plot, soil samples from first layer (0-10 cm) were taken for analyzing several soil variables. Evaluation of Forest site productivity by using classification and regression Tree algorithm showed that after pruning the full Tree, phosphorus, TRASP, clay and bulk density are effective variables, in order of relative importance, on site form and 62% variations in productivity can be explained by these variables. Using generalized linear model and evaluation criteria such as AIC, RMSE, R2 and adjusted R2, the performance of CART model was assessed. The results showed though CART techniques and the generalized linear model justify the same variability in Forest productivity but decision Tree technique in terms of AIC and BIC criteria is better than the GLM and as well as this technique is easier to interpret.