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.

In order to effectively manage soil and water resources, it is imperative to investigate wet aggregate stability (WAS) as a fundamental indicator for assessing soil structure and quality. In this study, machine learning techniques, specifically Random forest (RF) and Random forest optimized with genetic algorithm (GA-RF), were employed. The analysis focused on determining the texture, organic matter content, and lime characteristics of 55 soil samples collected from the Arsbaran forests. Utilizing various input combinations based on correlations with WAS, modeling was performed across seven distinct scenarios. Furthermore, three performance metrics including correlation coefficient (CC), normalized root mean square error (NRMSE), and Wilmot coefficient (WI) were utilized to evaluate the effectiveness of the models. The findings indicated that the RF5 model exhibited superior performance among the Random forest models, achieving NRMSE = 0.038, CC = 0.736, and WI = 0.789. Similarly, the GA-RF5 model, optimized through a genetic algorithm approach, demonstrated exceptional performance with NRMSE = 0.031, CC = 0.800, and WI = 0.842 when considering input percentages of sand, silt, and clay. Moreover, results from RF1 (NRMSE = 0.047, CC = 0.589, WI = 0.721) and GA-RF1 (NRMSE = 0.036, CC = 0.662, WI = 0.797) emphasized that clay content exhibited the strongest correlation with stability. Additionally, the incorporation of calcium carbonate equivalent in scenario 7 significantly enhanced model performance and positively influenced the prediction of wet aggregate stability. In summary, the hybrid model combining Random forest with a genetic algorithm is recommended for precise and reliable determination of wet aggregate stability in studies focusing on soil properties.

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.