The aim of the work is to predict complications in COVID-19 in the form of severe pneumonia based on clinical and laboratory data using machine learning (ML) methods. The severity of COVID-19 disease is based on the results of computed tomography (CT). The patient groups consisted of 31 patients with severe pneumonia (CT 2–4) and 113 patients with mild form (CT 0–1) and without pneumonia. The database included 105 clinical and laboratory parameters. The standard nonparametric criteria χ² and the Mann-Whittney criterion (U-test) with correction for multiple Bonferroni- Holm testing were applied. 13 significant indicators have been identified. Machine learning (ML) methods of the data analysis system («Data Master Azforus») were used and the best of them were applied in the form of an ensemble. ML methods have made it possible to build multifactorial nonlinear models for forecasting. For the entire follow-up period, the prediction result by the method of statistically weighted syndromes (SWS) reached a value of ROC AUC = 0.9. It was possible to make a fairly accurate prediction of severe pneumonia in COVID-19 based on the 26 most significant clinical and laboratory indicators. The clinical signs known to the attending physicians that determine the severity of pneumonia have been confirmed by ML methods. The approbation of the model proved its promise. The introduction of the model into practice will increase the accuracy and efficiency of diagnosis of severe pneumonia. The data analysis system («Data Master Azforus») will allow research doctors to create recommendation systems for predicting and diagnosing diseases

The development and implementation of artificial intelligence (AI) in all spheres of socio-economic life are currently relevant. Attitudes towards this phenomenon vary. Society’s attitude towards AI in education is ambiguous. Many, including highly respected and titled individuals, are negative towards this practice, warning of the capture of humanity by “smart machines.” In our opinion, the expansion of AI in education is inevitable. We define the basic concepts and analyze the positive and negative aspects of AI application in Russian Higher Education. Based on the research, we conclude that AI can undoubtedly help improve the quality of the educational process in all its aspects: efficiency of knowledge acquisition, automation of assessment and document management, individual approach, activation of students’ scientific activities, social adaptation, and psychological assistance. That is, it will help solve all pedagogical tasks facing the University and teachers. We substantiate the negative aspects of AI in higher education, such as new opportunities for academic fraud; lack of empathy in case of complete replacement of humans by AI; insufficient data security; lack of flexibility and adaptability to changes in the current moment; limitation of freedom of choice, limitations in creative fields. The paper notes the main neural networks used by modern students. The differences in the possibilities of using AI in Russia and Western countries are considered. The necessity of paying attention to the education of a moral and patriotic member of our society, along with improving the quality of education, is noted when improving the educational process. All technologies must operate under human control, and digital technologies must be improved to increase efficiency.

The article considers the transformation of the labor market and its impact on the demographic situation in connection with the transition of society to the information stage of development. The purpose of the study is to assess the role and prospects for the development of a Unified Digital Platform (UDP) in the social sphere in these processes. The research methodology is based on a comprehensive analysis of the relationship between the processes taking place in society — ​ the development of the gig economy, platform employment and self-employment and the challenges facing the Unified Digital Platform in the social sphere. The article considers the transformation of the labor market under the influence of technological changes and the prospects for the development of functions and services of the digital platform in the social sphere of Russia to ensure effective and comfortable interaction of pensioners and the self-employed with government services and the employment market in the new conditions. It is shown that attracting the self-employed and pensioners to legal labor activity with the help of the UDP has a positive effect on their financial stability, health and life expectancy. The article presents research data confirming that working pensioners are less likely to suffer from chronic diseases, depression and dementia, and also have a higher level of life satisfaction. The article also analyzes the functions of the Unified Digital Platform, such as simplified access to information, automation of work experience accounting, and integration with other government systems. These functions help reduce administrative barriers, motivate people to continue working, and improve the demographic situation. The article proposes an approach to quantitatively assessing the effectiveness of the Unified Digital Platform in the social sphere of Russia on the demographic situation based on a model using Bayesian trust networks. The results of the study can be used for expert and mathematical selection of alternative strategies for the development of Unified Digital Platform services in the social sphere.

The article considers the possibilities of applying modern security auditing with the involvement of information collected from open sources (OSINT — ​ Open Source Intelligence). The prerequisites for the use of OSINT and economic intelligence in security analysis are outlined, and the key factors influencing the formation of comprehensive information security measures are analysed. Particular attention is given to discussing how the use of open data can improve the effectiveness of the vulnerability identification process while complying with legal and ethical standards. An integrated approach to information security analysis, based on the competent use of data from public registries, social networks, professional communities and other open sources, is seen as one of the key factors that increase the resilience of businesses and government agencies. In the context of this artikle, special attention is paid to the development of recommendations for organising the process of assessing the security of objects on the basis of open sources, which makes it possible to combine aspects of information security and economic intelligence, increasing the overall level of information literacy and systemic risk management. The purpose of this artikle is to develop a set of recommendations on how to organise the process of security analysis of the object of assessment with reliance on data obtained from open sources. On the basis of the analysis, organisational, technical and procedural measures have been developed to improve the efficiency of information security of the object, as well as the possibilities of integrating the results of periodic research based on open sources of information and regular security checks into the overall information security system.

The article is devoted to the issues of automating the forecasting of the profitability of financial instruments by developing tools for interactive management of the parameters of stochastic models of profitability, calculations and visualization of results using the R programming language and the Shiny library. The purpose of the study is to create a web application that allows the User to manage all the key indicators affecting the final value of a financial instrument, to fine-tune the model by which the profitability trajectories will be built. To demonstrate the relationship of a particular model parameter to the final financial result, a graphical interpretation of the calculations is implemented, which allows real-time evaluation of the model’s response to a change in any parameter. The geometric Brownian motion model and the Monte Carlo method were used as a mathematical basis for building the web application. The research methodology is based on the use of mathematical modeling methods for forecasting asset prices under uncertainty. The main focus of the application is on interactive visualization of simulation results as a response to changes in the User interface parameters. This approach allows the user to work with the model parameters in real time, monitoring and controlling the system’s response to all of their actions. The application also implements functions for assessing possible scenarios for changing the value of assets, taking into account volatility, expected profitability, and a number of other characteristics. The results obtained during the development and practical use of the application show a high degree of control over the model’s behavior using visual elements of the application interface, clarity of presentation in assessing the expected profitability, and the ability to adapt the model to various financial instruments. The scenario options embedded in the application demonstrate how any minor changes in the input parameters affect the observed asset indicators, which emphasizes the importance of interactive stochastic analysis in the process of making investment decisions. The results of the study can be used for educational purposes to study tools for quantitative risk and profitability assessment, as well as in investment analysis. The developed application demonstrates the encapsulation of complex mathematical methods and concepts, make available for the User with clear and intuitive visual tools for studying financial risks and forecasting asset profitability.