Financial fraud detection is a challenging problem due to four primary reasons: the constantly changing fraudulent behavior, the lack of a mechanism to track fraud data, the specific limitations of available detection techniques (such as Machine learning algorithms), and the highly dispersed financial fraud dataset. Thus, it can be declared that teaching algorithms are complex. The current study used Machine learning techniques, including support vector Machine regression and boosted regression tree, to detect financial fraud in the Iranian stock market. The findings indicated that the boosted regression tree Machine model has the lowest RMSE. Furthermore, concerned with the sensitivity value of the models, the boosted regression tree model has the highest sensitivity in the sense that they had correctly detected the absence of financial fraud Tehran Stock Exchange market the Tehran Stock Exchange market. The boosted regression tree has the highest kappa coefficient indicating the appropriate performance of this model compared to other models used in the research.

Soil Temperature (ST) is critical for environmental applications. While its measurement is often difficult, estimation from environmental parameters has shown promise. The purpose of this study was to model ST in cold season from soil properties and environmental parameters. This study was conducted as a pot experiment in Ardebil, Iran. Automatic thermal sensors were installed at 5 and 10 cm depths. Besides, soil properties and environmental parameters were determined based on field and laboratory works. Machine learning methods including Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Interface System (ANFIS) were used for modeling ST. The air temperature was observed as the most effective factor in ST modeling. The relationship between soil and air temperature was stronger at 5 cm depth compared to 10 cm. The R2 between soil and air temperature was higher in the absence of sunlight than in its presence. The prediction of ANFIS (R2= 0. 96 and MAPE= 10. 5) was closer to the observed ST values compared to the ANN (R2= 0. 91 and MAPE= 35) and MLR (R2= 0. 57 and MAPE= 41). The results revealed the advantage of ANFIS method for ST modeling. This approach can be applied for soil depths and locations with data gap.

Background and ObjectivesThe use of geospatial techniques for mapping soils is broadly covered by the term digital soil mapping (DSM). Soil maps have considerable significance as basic maps in many environmental and natural resources studies. Digital soil maps are based on the relationship between environmental variables and soil properties. With the development of computers and technology, digital and quantitative approaches have been developed. Continuous utilization of agricultural lands regardless of the land suitability caused soil destruction. Also, incompetency in custom methods, invention geographic information system (GIS), and remote sensing (RS) techniques cause erupt and use of digital soil mapping. MethodologyThe study area is approximately 5000 ha which is located in the west of Heris region of East Azerbaijan province, Iran. In the first study, the potential of different models to predict soil classes at different taxonomic levels was investigated. According to semi-detailed soil, survey and using stratified random sampling method, 50 pedons and 50 augers with an approximate distance of 1000 m were excavated, described and soil samples were taken from different genetic horizons. Based on the pedon descriptions and soil analytical data, pedons were classified up to the family level. Different Machine learning techniques, namely boosted regression tree (BRT), random forest (RF), artificial neural networks (ANNs), and multinomial logistic regression (MLR) were used to test the predictive power for mapping the soil classes. After preparing the soil properties maps and checking their accuracy, these maps were used along with auxiliary parameters for estimating soil classes using an artificial neural network model in the R software. Finally, the accuracy and uncertainty of the model were evaluated by overall accuracy and confusion index, respectively. ResultsResults showed that the different models had the same ability for prediction of the soil classes across all taxonomic levels but a considerable decreasing trend was observed for their accuracy at subgroup and family levels. The terrain attributes were the most important auxiliary information to predict the soil classes up to the family level. The main goal of the second study was to predict soil surface properties (pH, electrical conductivity, gypsum, organic carbon, calcium carbonate equivalent, coarse fragments, and particle size distribution) using ANNs, BRT, generalized linear model (GLM), and multiple linear regression (MLR). Among the studied models, GLM showed the highest performance to predict most soil properties whereas the best model is not necessarily able to make an accurate estimation. Also, the terrain attributes were the most important environmental covariates to predict the soil classes in all taxonomic levels, but they could not display the soil variation entirely. This shows that the unexplained variations are controlled by unobserved variations in the environment, which can be due to the management over time. Results suggested that the DSM approaches have not enough prediction accuracy for the soil classes at lower taxonomic levels that focus on the soil properties affecting land use and management. Results showed that the entry of more details in the soil classification at the lower levels of the Soil Taxonomy system while increasing the number of classes, leads to decreasing the overall accuracy and increasing uncertainty. It is noticeable that the ANNs model has a good accuracy up to the great group level through the acceptable level of overall accuracy (i.e., 75 %), hence it has a high degree of uncertainty. Therefore, the accuracy of the model could not be effective in its selection through the modeling process; however, paying attention to its uncertainty is also very important along with the model error. ConclusionTerrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas. Further studies may still be required to distinguish new environmental covariates and introduce new tools to capture the complex nature of soils. Accordingly, we suggest using the other methods of soft computing for modeling in plain areas or low relief regions. Finally, the use of DSM methods is increasing over time and will eventually be considered as distinct and novel techniques.  Data Availability StatementData is available on reasonable request from the authors. AcknowledgementsThis paper is published as a part of a Master's thesis supported by the Vice Chancellor for Research and Technology of the Urmia University, Iran. The authors are thankful to the Urmia University for financial supports. Conflict of interestThe authors declare no conflict of interest. Ethical considerations The authors avoided data fabrication, falsification, plagiarism, and misconduct.

The application of Machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative Machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83. 5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour. com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.

Machine learning (ML) techniques have become a point of interest in medical research. To predict the existence of a specified disease, two methods K-Nearest Neighbors (KNN) and logistic regression can be used, which are based on distance and probability, respectively. These methods have their problems, which leads us to use the ideas of both methods to improve the prediction of disease outcomes. For this sake, first, the data is transformed into another space based on logistic regression. Next, the features are weighted according to their importance in this space. Then, we introduce a new distance function to predict disease outcomes based on the neighborhood radius. Lastly, to decrease the CPU time, we present a partitioning criterion for the data.

2Background and Aim: Kidney failure is a common and increasing problem in Iran and worldwide. Kidney transplantation is recognized as a preferred treatment method for patients with end-stage renal disease (ESRD). Machine learning, as one of the most valuable branches of artificial intelligence in the field of predicting patient outcomes or predicting various conditions in patients, has significant applications. The purpose of this research was to predict kidney transplant outcomes in patients using Machine learning. Materials and Methods: Since CRISP is one of the strongest methodologies for implementing data mining projects, it was chosen as the working method. In order to identify the factors affecting the prediction of kidney transplant outcomes, a researcher-created checklist was sent to some of nephrologists nationwide to determine the importance of each factor. The results were analyzed and examined. Then, using Python language and different algorithms such as random forest, SVM, KNN, deep learning, and XGBoost the data was modeled. Results: The final model was multilabel, capable of predicting various kidney transplant outcomes, including rejection probability, diabetic reactions, malignant reactions, and patient rehospitalization. After modeling the input data features, the model was able to predict the four kidney transplant outcomes such as rejection, diabetes, malignancy and readmission with an error rate of less than 0. 01. Conclusion: The high level of accuracy and precision of the random forest model demonstrates its strong predictive power for forecasting kidney transplant outcomes. In this study, the most influential factors contributing to patient susceptibility to the mentioned outcomes were identified. Using this Machine learning-based system, it is possible to predict the probability of these outcomes occurring for new cases