This topic is very relevant in the field of artificial intelligence as a direction of pattern recognition. In this work, the iris of the eye is considered as an image. Artificial intelligence makes this technology more accessible for use in CCTV cameras, smartphones and various areas of human activity. The article reflects the results of a study of methods and technologies of pattern recognition on the example of the human iris. The aim of the work was to study methods and technologies for human iris recognition and iris recognition of employees of a particular organization using EyeLock equipment by comparing segmentation results with Daugman standard segmentation. Comparison analysis of segmentation results with standard segmentation can be done by directly measuring the number of correctly segmented irises in both methods, or by indirectly measuring the effect of segmentation on iris recognition performance. The method using the Daugman integral-differential operator has the greatest efficiency. The performance of the neural network has been improved. To use a neural network to classify iris profiles, we selected sets of images (images per person) as training images, and the rest of the images were used as test images. Training time (in seconds): for the Daugman method 170.7, and for the parabolic method 204.7. The Daugman integro-differential operator is applied to the captured image to obtain the "maximum integral derivative of the contour" with ever-increasing radius on "successively decreasing scales" in three parameters: center coordinates and radius. Finding the maximum when the search coordinates deviate along an unwinding spiral. Methods and techniques for pattern recognition have been investigated using the human iris

Distance learning today allows you to create a system of mass continuous self-learning, universal exchange of information, regardless of the presence of time and space zones. For more effective online learning, it is necessary to introduce artificial intelligence technologies and methods for implementing learning systems. In this paper, the objects of research were: 1) the capabilities of the OSTIS open semantic technology, the capabilities of the IMS.OSTIS metasystem for organizing online learning; 2) machine learning method – classification. Results of the study: 1) an online course was organized using the IMS.OSTIS metasystem of the OSTIS open semantic technology; 2) for the analysis and visualization of training data, a machine learning method is implemented – classification. The results of the implementation of the online course were obtained using the semantic technology for designing an intelligent learning system: the IMS.OSTIS metasystem using the graphical semantic code SCg. The OSTIS kernel requires a machine with the Ubuntu operating system installed, which is a GNU/Linux distribution based on Debian GNU/Linux, an operating system based on the Linux kernel. The paper also shows an example of using the machine learning method – classification. This method allows you to classify data. Intelligent processing and visualization of data were carried out based on the results of testing students in order to classify them into letter categories A, B, C, D according to a set of features: scores and points of the average score. The high-level Python library Pandas was used, this is a library for data analysis. To visualize the results of data processing, the Matplotlib library in Python was used

To synthesize peer-reviewed studies from 2010–2024 on smart textbooks and intelligent tutoring systems, mapping the evolution of models and methods, subject-area coverage, and geographic trends, and cataloging reported advantages and limitations. A PRIS-MA-guided systematic review was conducted across Google Scholar, Scopus, and Web of Science using search terms centered on “smart/intelligent textbook” and “intelli-gent/smart tutor”; from 233 records, 26 studies met inclusion criteria and were coded by year, subject, country, models, and methods. Research activity was modest in 2010–2014, increased from 2015, and peaked in 2021–2022; General Education (n=16) and Program-ming (n=7) dominated subject coverage, while Mathematics, Chemistry, Pedagogy, Eng-lish, and Computer Science appeared infrequently (1–3 occurrences). Publications were concentrated in Asia—India (n=9), China (n=8), Japan (n=3)—with contributions from the United States (n=3) and select European countries. Student Model and Expert Model were the most common abstractions (3 uses each); adaptive learning systems (n=9) and knowledge-graph construction (n=6) were the leading methods. Reported benefits includ-ed individualized pacing and early difficulty detection, whereas recurrent concerns were methodological heterogeneity (12/26) and privacy/ethics (10/26). AI-enhanced education is shifting from heuristic sequencing toward semantically grounded, data-driven orchestra-tion that integrates learner and expert models with adaptive engines and knowledge graphs. To translate technical promise into durable and equitable learning gains, future implementations should broaden subject coverage, standardize reporting to enable me-ta-analysis, rigorously evaluate fairness and privacy, and embed explainable, teach-er-centered workflows across diverse contexts.

The paper examines modern models of education in higher educational institutions. The paper analyzes modern teaching models in higher education institutions in different countries. The article describes distance learning model, American learning model, traditional learning model, e-learning model, blended learning model.

The article is devoted to the problem of using mathematical methods for forecasting the tuberculosis epidemic in Kazakhstan using the example of the Karaganda region. The introduction of the article reflects the problem of forecasting tuberculosis in Kazakhstan. The main part of the article reflects the results of the analysis of mathematical methods for forecasting and data processing technology, describes the mathematical analysis of epidemiological indicators made with the SPSS statistical program, describes the factors affecting the incidence among contact persons, and calculates the correlation coefficient.The article shows the importance of mathematical modeling and the importance of developing a specific mathematical model that describes the spread of infection among the population.