This article discusses common approaches to the development of ANPR systems. The main idea of the article is the parallel use of several different classifiers for OCR and fusion of obtained data for improving the recognition accuracy.

The aim of this study is a comparative evaluation of the effectiveness of YOLO and RT-DETR family models for the automatic recognition and localization of meniscus tears in knee joint MRI images. The experiments were conducted on a proprietary annotated dataset consisting of 2000 images from 2242 patients from various clinics. Based on key performance metrics, the most effective representatives from each family, YOLOv8-x and RT-DETR-l, were selected. Comparative analysis based on training, validation, and testing results showed that YOLOv8-x delivered more stable and accurate outcomes than RT-DETR-l. The YOLOv8-x model achieved high values across key metrics: accuracy—0.958, recall—0.961; F1-score—0.960; mAP@50—0.975; and mAP@50–95—0.616. These results demonstrate the potential of modern object detection models for clinical application, providing accurate, interpretable, and reproducible diagnosis of meniscal injuries.

This article discusses about the evaluation metrics of original image quality as the basis for selecting the optimal preprocessing scenario and recognition algorithms.

This paper explores the construction of a model for evaluating the digital ecosystem within a university, with a focus on identifying key factors influencing satisfaction with the implementation of new digital processes in the educational environment. The study employs mathematical methods, specifically factor analysis, to gauge the impact of these digital processes on the overall educational landscape. A questionnaire was designed to collect relevant data, and structural equation modeling, utilizing the asymptotically distribution-free estimation method with Grammian in STATISTICA software, was employed for survey result processing. The proposed model aims to provide insights into the dynamics of a university's digital ecosystem, offering a systematic approach to assess satisfaction levels and comprehend the implications of integrating novel digital processes within the educational framework. Mathematical methods, including factor analysis, add a quantitative dimension to the evaluation process, enabling a comprehensive understanding of the relationships between various factors. The study's methodology ensures a rigorous and systematic analysis of survey data, enhancing the reliability of the findings. The developed model and methodology contribute to advancing our understanding of the digitalization of university environments, providing valuable tools for decision-makers in shaping effective strategies for integrating digital processes in education. The study conducted a survey with 350 participants, including university staff and students. A questionnaire with 17 questions, both open and closed-ended, was developed to collect data. The authors employed structural equation modeling, specifically the asymptotically distribution-free estimation method, for data processing. The study's a posteriori model illustrates the structure of interaction factors influencing satisfaction with the university's digital ecosystem.

New approaches and methods based on machine learning technologies make it possible to identify not only the spread of earthquakes, but also to establish hidden patterns that allow further assessment of any risks associated with their occurrence. In this article, the clustering algorithms of K-means and K-medoids are applied for the analysis of seismic data recorded on the territory of the Republic of Kazakhstan. Using the Elbow and Silhouette methods, the optimal value of K clusters was determined, which was later used in classifying a data set using cluster analysis methods. The results of seismic data classification by clustering algorithms are in line with expectations. However, when measuring the quality of clustering, the accuracy of the model by the K-means method exceeded the accuracy of the K-medoids model, and the scoring value by the K-means method is ahead of the value by the K-medoids method. In addition, the presented results of descriptive statistics allowed to carry out a more in-depth analysis of the characteristics of each cluster.

This article is devoted to creating an intellectual model for assessing the level of formation of students’ competencies based on learning outcomes, taking into account the requirements of the professional environment. The authors of the article proposed a method for constructing a fuzzy cognitive map and a model for assessing the level of competence formation based on a fuzzy approach. The main concepts of the cognitive map are defined as disciplines, learning outcomes, and competencies. The article provides an example of a cognitive map for the educational program in the Information Technology direction. The determination of the link weights between the concepts of the map was implemented in an expert way. The model for assessing the level of competence formation is based on the fuzzy set theory. The article defines the following parameters of the model: linguistic variables, membership functions and similarity measures. A universal five-level Dreyfus model was used to describe the level of competence formation. Based on the proposed model, the “Digital Profile” information system was developed and implemented as a web application. The article presents the main mechanisms of information system design: information objects are defined, the logical scheme of the database is described, the scheme of the algorithm for creating a digital profile is described and the functional windows of the web application are described. The developed “Digital Profile” information system makes it possible to assess the achievements of mastering the competencies of educational programs, as well as to assess the quality of student training.