Globally, assessing sustainable development methodology is kept in sustainable society index (SSI) format, but at the level of meso- and microsystems it remains undeveloped. The aim of the study is to typologize innovative mesosystems in Russian industry in the context of sustainable development based on the CART algorithm and to develop an algorithm for identifying priority areas of sustainable development. The research methods applied included formalization, a systematic approach, and the CART algorithm (calculation of the Gini index, training sample segmentation, the use of a recursive function and regression assessment). As a result of the study, the algorithm for the differentiated identification of innovative mesosystems sustainable development priority directions in industry based on the unique author’s methodology (ISDI) is proposed. The predominance of mesosystems with weak level of sustainable development requiring state support in favor of such mesosystems restructure is revealed. The novelty of the research lies in the development of new science-based solutions to ensure an accelerated transition of industry to the path of sustainable development. The difference of the author’s approach from the provisions known in science is the inclusion of environmental innovations in the mechanism for managing the sustainable development of innovative mesosystems and subsequent accounting in the process of mathematical processing of an array of data, which determines the uniqueness of the constructed decision trees.

The authors define the goals of digital transformation of countries, highlight the main distinctive elements of their digitalization and classify factors that affect the speed of digital transformation. They assess the development level of digital economy in Russia, Ukraine and developed countries in 2007-2020 according to the International Digital Economy and Society Index (I-DESI) and the Global Innovation Index (GII). The authors provide information about changes in the ranking of individual countries according to the GII and identify top ten countries in each group. The authors compare the most popular innovative technological trends, present the expected payback periods for investments in digital solutions in Russia and the world and determine the barriers that hinder the development of digitalization in the countries and companies. The conducted analysis shows that against the background of a significant slowdown in the global economic growth, there are concerns about a reduction in R&D, venture capital and intellectual property.

The purpose of the article The purpose of the study is to determine the features of factors’ development and systematization affecting the degree of innovation activity and efficiency of projects in the Russian regions. Materials and methods: The main research methods used in the article are the method of description, which allowed determining of the level and trends of Russian regions’ innovative development; the method of cluster analysis for regions’ classification by innovative activity’s parameters in order to identify the characteristics and determine the directions of the target differentiated approach to the development of innovation. Results of the research: The article presents the analysis of innovative development dynamics of the Russian regions’ industrial sector; the tendencies of scientific and innovative activity’s financing in the Federal districts; the regions’ classification by the parameters of innovative activity in order to identify the characteristics of innovations’ development and commercialization. Applications: This research can be used for universities, teachers, and students. Novelty/Originality: In this research, the model of differentiated approach problems to innovative development management in Russian regions is presented in a comprehensive and complete manner.

The planning and management of traffic flow networks with multiple input data sources for decision-making generate the need for a mathematical approach. The program of measures for the development of the transport infrastructure of the Russian Federation provides for the selection of pilot regions for the creation of intelligent transportation systems. With extensive knowledge of theoretical and applied mathematics, it is important to select and adapt mathematical methods for solving problems. In this regard, the aim of the study is to develop and validate an algorithm for solving the problem of classifying objects according to the potential of creating intelligent transportation systems. The main mathematical apparatus for classification is the «random forest» machine learning algorithm method. A bagging machine learning meta-algorithm for high accuracy of the algorithm was used. This paper proposes the author’s method of sequential classification analysis for identifying objects with the potential to create intelligent transportation systems. The choice of using this method is justified by its best behavior under the large number of predictor variables required for an objective aggregate assessment of digital development and quality of territories. The proposed algorithm on the example of Russian regions was tested. A technique and algorithm for statistical data processing based on descriptive analytics tools have been developed. The quality of the classification analysis algorithm was assessed by the random forest method based on misclassification coefficients. The admissibility of retrained algorithms and formation of a «fine-grained» «random forest» model for solving classification problems under the condition of no prediction was proven to be successful. The most productive models with the highest probability of correct classification were «reached» and «finalized» on the basis of logistic regression analysis of relationships between predictors and categorical dependent variables. The regions of class 1 with «high potential for the creation of intelligent transportation systems» are most likely to be ready for the reorganization of infrastructure facilities; the introduction of digital technologies in the management of traffic flows was found.