Modern methods of studying the concepts of «tourism» and «tourist service»: from theory to practice

DOI: 10.33917/mic-3.104.2022.38-45

In a scientific article, based on the relativistic theory, a terminological interpretation of the concepts of «tourism», «tourist service» and their specific classification is presented. Varieties of domestic tourism have been supplemented with a shift in focus to regional development in six areas (business tourism; ecological tourism; cultural and educational tourism; autotourism-youth-family tourism; rural tourism and sanatorium tourism). Compiled rating of competitiveness of tourist services on the example of the Republic of Tatarstan.

References:

1. Zhukovskaya I.V. Systematization of scientific approaches to managing competitiveness in the service sector: economic essence, criteria. Microeconomics. 2020;2:29-37.

2. Zhukovskaya I.V. The specifics of the study of the service market on the example of the Republic of Tatarstan. Microeconomics. 2020;5:93-98.

3. Zhukovskaya I.V. Segmentation of the service market: problems, solutions. Microeconomics. 2021;2:32-37.

4. Sizikova V.V. Increasing the competitiveness of service enterprises: dis. cand. economy Sciences: 08.00.05. St. Petersburg, 2019. 189 p.

5. Official information of the Federal Agency for Tourism. URL: tourism.gov.ru

6. Decree of the Government of the Russian Federation of September 20, 2019 No. 2129-r. Strategy for the development of tourism in the Russian Federation for the period up to 2035. – Access from legal reference system ConsultantPlus.

7. Ragimkhanov A.V. Systematization of scientific approaches to the study of the competitiveness of services: from theory to practice / A.V. Ragimkhanov, I.V. Zhukovskaya. Microeconomics. 2022;1:75-78.

Classification of Artificial Intelligence Systems

DOI: 10.33917/es-6.172.2020.58-67

The article considers the classification of Artificial Intelligence (AI) systems. The role of AI has increased significantly recently in all areas of life. The use of AI in public administration, in production, in medicine, in the military, in the social sphere, etc., raised a number of questions related to the definition of AI and classification of AI systems. Classification of AI is necessary to understand the role of AI in the digital economy. Classification becomes important in the context of intensive development of international standards for AI systems and knowledge-based systems (expert, neural, multi-agent, cyber-physical systems and systems based on the industrial Internet)

Innovations in Combating the Shadow Financial Segment in Recent Years on the Example of Russia

DOI: 10.33917/es-6.172.2020.138-143

The article notes that in relation to the growth of the shadow component in the Russian economy, identifying the essence of the shadow economy is especially relevant. The author addresses shadow processes as the basis of the shadow economy, their place in its structure, basic principles of formation and classification according to various criteria.

Forecasting bankruptcies of counterparties based on payment discipline data

DOI: 10.33917/mic-4.93.2020.47-56

In this article, we study the problem of forecasting bankruptcy of firms using data on payment discipline. Most previous researchers used the balance sheet as a data source, while data on payment discipline will reduce the time before making a decision on the firm, as well as obtain reliability ratings based on other types of data. To predict bankruptcy of the firms proposed a new method of work with highly unbalanced data, which consists in training the classifiers on the automatically generated sub-sample and averaging the obtained results. Random forest served as a classifier for subsamples, and AUC-score was used to check the quality of the model, which showed good results.

Analysis approaches to assessing digital inequality of education systems

DOI: 10.33917/mic-1.90.2020.32-49.

In the new economic conditions such as globalization, the information revolution, and the growing need for a highly skilled workforce, countries are increasingly shifting priority to education. Digitalization, as a modern trend in the development of the world economy and society, also has an influence on the education system. However, it is difficult to make cross-country comparisons in the area of digital competitiveness because there are no common approaches to assessing the digital divide, which is shifting from the gap in access and connectivity to ICT to the knowledge gap. To consider various aspects of digitalization of education systems, it is important to identify the countries that are most advanced in this issue. With this in mind, to determine global trends of digital education it is advisable to carry out their classification by characteristics reflecting the differences between these digital systems. However, the absence of such system-forming features makes classification difficult. Therefore, it is important to identify factors that can serve as a basis for classifying the world’s education systems by their level of digitalization. There are no methodological approaches to measuring digital inequality in education systems in international practice. This work is an attempt to identify indicators that would allow us to compare global educational models in terms of creating conditions for learning using ICT. As a result, the relationship between the digital gap in access and connection to ICT and the digital inequality of national education systems is determined, and the comparison of national education systems using digital gap indicators is carried out.