The article solves the problem of automated generation of user roles using machine learning methods. To solve the problem, cluster data analysis methods implemented in Python in the Google Colab development environment are used. Based on the results obtained, a method for generating user roles was developed and tested, which allows reducing the time for generating a role-based access control model.

Keywords: machine learning, role-based access control model, clustering, k-means method, hierarchical clustering, DBSCAN method

The article solves the problem of diabetes mellitus diagnostics. Diabetes mellitus is characterized by high prevalence and significant costs for treatment and prevention of complications. This disease worsens the quality of patient's life, limiting their daily activities and functioning.To solve it, it is proposed to construct and use a neuro-fuzzy model. To train the model, the search and preparation of initial data for analysis were performed. The data were obtained from the publicly available Kaggle source. The data for analysis was prepared on the basis of the analytical platform Deductor. From the prepared data set, training and testing samples were formed, used to construct the model. Comparison of the obtained results with the known results of other authors allowed us to conclude that the model is adequate and can be used in practice.

Keywords: neuro-fuzzy model, fuzzy neural network, diabetes mellitus, modeling, diagnostics, machine learning

This work solves the problem of increasing the effectiveness of educational activities by predicting student performance based on external and internal factors. To solve this problem, a model for predicting student performance was built using the Python programming language. The initial data for building the decision tree model was taken from the UCI Machine Learning Repository platform and pre-processed using the Deductor Studio Academic analytical platform. The results of the model are presented and a study was conducted to evaluate the effectiveness of predicting student performance.

Keywords: forecasting, decision tree, student performance, influence of factors, effectiveness assessment

The article describes a technique for constructing a non-fuzzy model for selecting contour points on an image. The technique includes the following steps: the formation of linguistic variables “pixel brightness difference” and “a sign that a pixel belongs to a contour”, the formation of a knowledge base of a neuro-fuzzy model using a binary image, the formation of a training set using both grayscale and contour images, training a neuro-fuzzy model using genetic algorithm. A feature of the presented genetic algorithm is - checking the conditions for the correctness of the values of the parameters of the membership functions obtained during the generation of chromosomes. Described the structure of a neuro-fuzzy model for making a decision about whether a pixel belongs to a contour. Presented the result of applying a neuro-fuzzy model for constructing image contours.

Keywords: neuro-fuzzy model, contour image, contour extraction, contour pixel, linguistic variable, fuzzy set, membership function, genetic algorithm, Tsukamoto inference, neuro-fuzzy model learning

The article describes a technique of developing neural network models of controllers for controlling a technical object, approximating the relationship between the control action and the deviation of the state of the object from the setting action, its speed and acceleration. The application of a technique for controlling the temperature of a water bath water heater is considered. The technical object is described by a second-order differential equation and has a smooth monotonic behavior.

Keywords: technical object, water bath, water heater, neural regulator, control, object behavior, model, neural network, training set, perceptron