Digital twins and machine learning, two contemporary information technology, have opened up new possibilities for the digital display, visualization, and monitoring of industrial processes. This research suggests utilizing machine learning, visualization, and monitoring approaches to analyze the use of digital twins in industrial process control. This novel method integrates real-time performance data and machine learning for monitoring, visualization, and prediction. In particular, trends and preferences are found and future situations are predicted by machine learning approaches. A variety of situations are created and assessed with the use of digital twins, and information is gathered to train machine learning algorithms on actual data.

In modern society, the health of students is one of the priorities, which is receiving more and more attention. Deviations or any negative changes in health status can negatively affect their academic performance, as well as their quality of life. Therefore, the development of an information and analytical system for monitoring the health of students is a hot topic not only in Kazakhstan. The article presents research as a component for solving this problem. Today, society is paying more and more attention to caring for the health of students, realizing that their well-being is directly reflected in academic and personal indicators. This article describes an approach to creating an information and analytical system based on machine learning and data analysis methods to assess and identify key factors affecting the quality of life of students.

The aim of this work is demonstration of usefulness and efficiency of statistical methods of text processing for automatic construction of concept maps of the pre-determined domain. Statistical methods considered in this paper are based on the analysis of co-occurrence of terms in the domain documents. To perform such analysis, at the first step we construct a term-document frequency matrix on the basis of which we can estimate the correlation between terms and the designed domain. At the second step we go on from the term-document matrix to the term-term matrix that allows to estimate the correlation between pairs of terms. The use of such approach allows to define the links between concepts as links in pairs which have the highest values of correlation. At the third step, we have to summarize the obtained information identifying concepts as nodes and links as edges of a graph and construct a concept map as resulting graph.