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.

Introduction     Considering the importance and high cost of construction and maintenance of offshore platforms for the purpose of oil and gas extraction, and considering the fact that in case of failure, they can cause many environmental disasters, technical inspection of their structural condition is of serious importance. In addition to the high cost, this does not cover all aspects and it is very difficult to detect the failure in this case. Due to the repetitive nature of most of the environmental loads in the seas, these structures are constantly exposed to multiple and repetitive loadings. The phenomenon of fatigue is one of the effective factors in the health of these types of structures, so that during the past decades, the offshore industry has witnessed unfortunate events that often occurred due to the phenomenon of fatigue. One of the unpleasant cases can be mentioned the disaster of the Norwegian semi-submerged oil platform in the North Sea, named as the Kyland Alexandria, in which 123 people of the platform's crew lost their lives. One of the main braces connected to the base of the pontoon was completely broken and separated from the platform, causing the platform to completely overturn. The semi-submersible rig Sedo 135, which began operating in the Gulf of Mexico in 1965, suffered a fatigue failure in one of its rigs in 1967 after two years. One of the most widely used platforms in the Persian Gulf is the fixed platform of the stencil or jacket type, which is a steel structure that has braces and a deck, and foundations that are fixed on the sea floor by numerous piles. Fatigue cracks are the main failure factor in fixed jacket platforms (Ibrion et al., 2020).

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimensionality of datasets in terms of reduced feature set. Feature selection improves the performance of classification accuracy particularly performing with less number of features in decision making process. In this paper, Random forest (RF) is employed for the diagnosis of cardiovascular disease. The first phase of the proposed system aims at constructing various feature selection algorithms such as Principal Component Analysis (PCA), Relief-F, Sequential Forward Floating Search (SFFS), Sequential Backward Floating Search (SBFS) and Genetic algorithm (GA) for reducing the dimension of cardiovascular disease dataset. The second phase switched to model construction based on RF algorithm for cardiovascular disease classification. The outcome shows that the combination with GA and RF delivered the highest classification accuracy of 93. 2% by the help of six features.

Introduction     Considering the importance and high cost of construction and maintenance of offshore platforms for the purpose of oil and gas extraction, and considering the fact that in case of failure, they can cause many environmental disasters, technical inspection of their structural condition is of serious importance. In addition to the high cost, this does not cover all aspects and it is very difficult to detect the failure in this case. Due to the repetitive nature of most of the environmental loads in the seas, these structures are constantly exposed to multiple and repetitive loadings. The phenomenon of fatigue is one of the effective factors in the health of these types of structures, so that during the past decades, the offshore industry has witnessed unfortunate events that often occurred due to the phenomenon of fatigue. One of the unpleasant cases can be mentioned the disaster of the Norwegian semi-submerged oil platform in the North Sea, named as the Kyland Alexandria, in which 123 people of the platform's crew lost their lives. One of the main braces connected to the base of the pontoon was completely broken and separated from the platform, causing the platform to completely overturn. The semi-submersible rig Sedo 135, which began operating in the Gulf of Mexico in 1965, suffered a fatigue failure in one of its rigs in 1967 after two years. One of the most widely used platforms in the Persian Gulf is the fixed platform of the stencil or jacket type, which is a steel structure that has braces and a deck, and foundations that are fixed on the sea floor by numerous piles. Fatigue cracks are the main failure factor in fixed jacket platforms (Ibrion et al., 2020).

It is necessary to evaluate sustainable spatial allocation of afforestation. For this purpose, this study was conducted in the Kan watershed of Tehran province to assess the suitability of land for afforestation. First, suitable tree species were chosen based on land characteristics of study area and purpose of restoration. Then, the ecological demands of tree species were investigated and effective Indicators which affect the evaluation process were identified. After processing, classification and integration of spatial layers in GIS using the system analysis method, a Random forest algorithm was trained and suitability map of afforestation was produced. Results show that Random forest method has a high accuracy in predicting suitable areas for afforestation. Also, 2116 ha of study area is moderately suitable for afforestation. Based on Boruta algorithm Soil depth, growing season precipitation, elevation, soil texture, slope and aspect are considered as the most important to the least important features, respectively and it is not necessary to carry out weighting methods for evaluation of afforestation capability. Generally, Random forest method can be used as a capable way to prepare ecological capability maps.

Background and Objectives: Reference evapotranspiration (ET0) is an important parameter in the interactions among soil, vegetation, atmosphere, surface energy and water. Direct measurement of evapotranspiration values is costly and time consuming. On the other hand, modeling this complex process in which many variables interact with each other is not feasible without considering multiple assumptions. In this regard, the FAO Penman-Monteith method is used in a wide range of climatic and environmental conditions. One of the weaknesses of FAO Penman-Monteith method is its dependence on various meteorological parameters. Therefore, it is necessary to implement methods with lower meteorological variables that can estimate ET0 with suitable accuracy. Thus, in the present study, an attempt was made to estimate ET0 with acceptable accuracy using machine learning models. Methodology: In the present study, daily meteorological parameters in the time period of 2000-2020 including maximum and minimum air temperature (Tmax, Tmin), mean temperature (T), wind speed (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin) and sunshine hours (n) were obtained on a daily basis in three stations of East Azerbaijan province (Tabriz, Sarab, and Maragheh). Moreover, six scenarios were defined as input combinations. Then, using Random forest (RF) method in two cases: Single Random forest and using the genetic algorithm (GA) to optimize its effective parameters with considering the FAO Penman-Monteith model as a basis, the machine learning models were calibrated and validated for estimating ET0 values at studied stations. Furthermore, the performance of empirical equations in three groups based on temperature (Hargreaves, Blaney-Criddle and Romanenko), radiation (Irmak) and mass transfer (Meyer) were also investigated. It should be noted that 75% of the data were considered for calibration and 25% for the validation of machine learning methods. Finally, using the statistical criteria of correlation coefficient (CC), scattered index (SI) and Willmott’s Index of agreement (WI), a suitable machine learning method was introduced to estimate the reference evapotranspiration. Also, the most suitable combination of meteorological parameters for ET0 estimation was suggested. Findings: The obtained results showed that in all studied stations, scenario 6 has the best performance, either in the case of single Random forest (RF) or in the case of Random forest optimized by genetic algorithm (GA-RF). Meteorological parameters of this scenario include minimum and maximum air temperature, minimum and maximum relative humidity, sunshine hours and wind speed. By optimizing the RF-6 parameters with the genetic algorithm at Tabriz station, the statistical criteria were improved (CC from 0.990 to 0.991, SI from 0.103 to 0.098). At Sarab station, the CC was increased from 0.980 to 0.982, the SI was decreased from 0.140 to 0.132 and the WI was increased from 0.989 to 0.990. At Maragheh station, CC was increased from 0.990 to 0.991, SI was decreased from 0.103 to 0.098 and WI remained unchanged at 0.995. In general, the decreasing trend of the scattered index for RF method from scenarios 1 to 6 can be understood by increasing the input parameters of the Random forest method. Among the three groups of empirical methods based on air temperature, radiation and mass transfer for estimating ET0, the best performance was seen for the Blaney-Criddle method based on air temperature. In all studied stations, the GA-RF model showed better performance than the empirical methods. Also, GA-RF-5 with similar meteorological parameters with Blaney-Criddle method provided accurate ET0 estimations.Conclusion: Determining the amount of daily evapotranspiration and consequently accurate estimation of water requirement of plants provide the basis for proper designing of irrigation systems by reducing installation costs and providing a suitable program for the use of water resources in the agriculture sector. So, in the present study, meteorological data from Tabriz, Sarab and Maragheh stations were used to evaluate the ability of machine learning methods including RF and GA-RF to estimate the values of reference evapotranspiration. The results showed the high accuracies of RF-6 and GA-RF-6 for all studied stations and Belany-criddel among the empirical models. In a more detailed look, the genetic algorithm had positive effects on increasing the model accuracies by reducing scattered index of GA-RF scenarios 1, 4, 5 and 6 in Tabriz and Maragheh stations as well as scenarios 1, 5 and 6 at Sarab station. Finally, it can be concluded that both RF and GA-RF models provided the most accurate estimates of daily reference evapotranspiration in the East Azerbaijan province.