Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model
Increased use of modern mathematical algorithms based on artificial intelligence determined the relevance of this study, which is important for predicting the sustainable development of the country's economy in general and its banking sector in particular. To achieve the purpose of the rese...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-12-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6848 |
| _version_ | 1797372728629526528 |
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| author | Nikolay Lomakin Anastasia Kulachinskaya Vera Tsygankova Ekaterina Kosobokova Oksana Minaeva Valentina Trunina |
| author_facet | Nikolay Lomakin Anastasia Kulachinskaya Vera Tsygankova Ekaterina Kosobokova Oksana Minaeva Valentina Trunina |
| author_sort | Nikolay Lomakin |
| collection | DOAJ |
| description | Increased use of modern mathematical
algorithms based on artificial intelligence determined the relevance of this
study, which is important for predicting the sustainable development of the
country's economy in general and its banking sector in particular. To achieve
the purpose of the research, the presented work used methods such as
monographic, analytical, statistical, cognitive model, and artificial
intelligence system "Random Forest". The aim of the study is to prove
or disprove the hypothesis that, using a cognitive model, using the Random
Forest ML model, it is possible to obtain an accurate forecast of the value of
the "sustainability coefficient", reflecting the stability of the
domestic economy. The scientific novelty of the study is due to the fact that
the author's approach is proposed for indicating the crisis state of the
economy through the calculation and neural network forecasting by the machine
learning model "Random Forest" of the "Stability
Coefficient" of the economy, which is calculated as the quotient of dividing
the profit index of the banking system to the GDP growth index. The possibility
of practical application in the banking sector determines the practical
significance of the conducted scientific research since the approach proposed
by the authors regarding the formation of a forecast of the “sustainability
coefficient” can be successfully used to support managerial decision-making at
the strategic level in the banking system. A hypothesis was put forward and
proven that based on the use of a digital cognitive model and the Random Forest
ML system, a forecast of economic stability can be successfully generated. |
| first_indexed | 2024-03-08T18:40:01Z |
| format | Article |
| id | doaj.art-88476497256f49808412b173d97de8f9 |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-03-08T18:40:01Z |
| publishDate | 2023-12-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-88476497256f49808412b173d97de8f92023-12-29T09:10:47ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002023-12-011481800180910.14716/ijtech.v14i8.68486848Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive ModelNikolay Lomakin0Anastasia Kulachinskaya1Vera Tsygankova2Ekaterina Kosobokova3Oksana Minaeva4Valentina Trunina5Volgograd State Technical University, Faculty of Economics and Management, Department of Management and Finance of Production Systems, Department of Economics and Entrepreneurship ave. V.I. Lenina, 2St. Petersburg Polytechnic University, Graduate school of industrial economics Polytechnicheskaya, 29, 195251, St. Petersburg, RussiaVolgograd State Technical University, Faculty of Economics and Management, Department of Management and Finance of Production Systems, Department of Economics and Entrepreneurship ave. V.I. Lenina, 2Volgograd branch of the PRUE G.V. Plekhanov, Department of Economics st. Volgodonskaya, 11, Volgograd, 400066, RussiaVolgograd State Technical University, Faculty of Economics and Management, Department of Management and Finance of Production Systems, Department of Economics and Entrepreneurship ave. V.I. Lenina, 2Volgograd State Technical University, Faculty of Economics and Management, Department of Management and Finance of Production Systems, Department of Economics and Entrepreneurship ave. V.I. Lenina, 2Increased use of modern mathematical algorithms based on artificial intelligence determined the relevance of this study, which is important for predicting the sustainable development of the country's economy in general and its banking sector in particular. To achieve the purpose of the research, the presented work used methods such as monographic, analytical, statistical, cognitive model, and artificial intelligence system "Random Forest". The aim of the study is to prove or disprove the hypothesis that, using a cognitive model, using the Random Forest ML model, it is possible to obtain an accurate forecast of the value of the "sustainability coefficient", reflecting the stability of the domestic economy. The scientific novelty of the study is due to the fact that the author's approach is proposed for indicating the crisis state of the economy through the calculation and neural network forecasting by the machine learning model "Random Forest" of the "Stability Coefficient" of the economy, which is calculated as the quotient of dividing the profit index of the banking system to the GDP growth index. The possibility of practical application in the banking sector determines the practical significance of the conducted scientific research since the approach proposed by the authors regarding the formation of a forecast of the “sustainability coefficient” can be successfully used to support managerial decision-making at the strategic level in the banking system. A hypothesis was put forward and proven that based on the use of a digital cognitive model and the Random Forest ML system, a forecast of economic stability can be successfully generated.https://ijtech.eng.ui.ac.id/article/view/6848cognitive modelingdl-model random forestformation of sustainability forecastsustainability of the country's economy |
| spellingShingle | Nikolay Lomakin Anastasia Kulachinskaya Vera Tsygankova Ekaterina Kosobokova Oksana Minaeva Valentina Trunina Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model International Journal of Technology cognitive modeling dl-model random forest formation of sustainability forecast sustainability of the country's economy |
| title | Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model |
| title_full | Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model |
| title_fullStr | Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model |
| title_full_unstemmed | Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model |
| title_short | Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model |
| title_sort | forecast of stability of the economy of the russian federation with the ai system decision tree in a cognitive model |
| topic | cognitive modeling dl-model random forest formation of sustainability forecast sustainability of the country's economy |
| url | https://ijtech.eng.ui.ac.id/article/view/6848 |
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