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http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4122Повний запис метаданих
| Поле DC | Значення | Мова |
|---|---|---|
| dc.contributor.author | Ків, Арнольд Юхимович | - |
| dc.contributor.author | Hryhoruk, Pavlo | - |
| dc.contributor.author | Khvostina, Inesa | - |
| dc.contributor.author | Solovieva, Victoria | - |
| dc.contributor.author | Соловйов, Володимир Миколайович | - |
| dc.contributor.author | Семеріков, Сергій Олексійович | - |
| dc.date.accessioned | 2020-12-24T16:56:09Z | - |
| dc.date.available | 2020-12-24T16:56:09Z | - |
| dc.date.issued | 2020-10-26 | - |
| dc.identifier.citation | Kiv A. Machine learning of emerging markets in pandemic times [Electronic resource] / Arnold Kiv, Pavlo Hryhoruk, Inesa Khvostina, Victoria Solovieva, Vladimir Soloviev, Serhiy Semerikov // Machine Learning for Prediction of Emergent Economy Dynamics 2020 : Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020 / Edited by : Arnold Kiv // CEUR Workshop Proceedings. – 2020. – Vol. 2713. – Pp. 1-20. – Access mode : http://ceur-ws.org/Vol-2713/paper00.pdf | uk_UA |
| dc.identifier.issn | 1613-0073 | - |
| dc.identifier.uri | http://ceur-ws.org/Vol-2713/paper00.pdf | - |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4122 | - |
| dc.identifier.uri | https://doi.org/10.31812/123456789/4122 | - |
| dc.description | 1. Bielinskyi, A., Semerikov, S., Serdiuk, O., Solovieva, V., Soloviev, V., Pichl, L.: Econophysics of sustainability indices. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 2. Danylchuk, H., Ivanylova, O., Kibalnyk, L., Kovtun, O., Melnyk, T., Serdiuk, O., Zaselskiy, V.: Modelling of trade relations between EU countries by the method of minimum spanning trees using different measures of similarity. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEURWS.org, online (2020, in press) 3. Danylchuk, H., Kibalnyk, L., Kovtun, O., Kiv, A., Pursky, O., Berezhna, G.: Modelling of cryptocurrency market using fractal and entropy analysis in COVID-19. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 4. Derbentsev, V., Matviychuk, A., Datsenko, N., Bezkorovainyi, V., Azaryan, A.: Machine learning approaches for financial time series forecasting. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEURWS.org, online (2020, in press) 5. Hobyr, I., Babenko, V., Kafka, S., Bui, Y., Savko, O., Shmeltser, E.: Use of simulation modeling for predicting optimization of repair works at oil and gas production enterprises. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 6. Horal, L., Khvostina, I., Reznik, N., Shyiko, V., Yashcheritsyna, N., Korol, S., Zaselskiy, V.: Predicting the economic efficiency of the business model of an industrial enterprise using machine learning methods. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 7. Horoshkova, L., Khlobystov, I., Volkov, V., Holovan, O., Markova, S., Golub, A., Oliynyk, O.: Asymptotic methods in optimization of multi-item inventory management model. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 8. Hryhoruk, P., Grygoruk, S., Khrushch, N., Hovorushchenko, T.: Using non-metric multidimensional scaling for assessment of regions' economy in the context of their sustainable development. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 9. Hrytsiuk, P., Babych, T.: The cryptocurrencies risk measure based on the Laplace distribution. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2- MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 10. Ivanov, M., Ivanov, S. Cherep, O., Terentieva, N., Maltiz, V., Kaliuzhna, I., Lyalyuk, V.: Fuzzy modelling of Big Data of HR in the conditions of Industry 4.0. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 11. Kaminskyi, A., Nehrey, M., Rizun, N.: The impact of COVID-induced shock on the riskreturn correspondence of agricultural ETFs. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEURWS.org, online (2020, in press) 12. Khrushch, N., Hryhoruk, P., Hovorushchenko, T., Lysenko, S., Prystupa, L., Vahanova, L.: Assessment of bank's financial security levels based on a comprehensive index using information technology. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 13. Khvostina, I., Oliinyk, V., Yatsenko, V., Mykhailyshyn, L., Berezhnytska, U.: Modeling the optimal management of the distribution of profits of an oil and gas company taking into account risks. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2- MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 14. Khvostina, I., Semerikov, S., Yatsiuk, O., Daliak, N., Romanko, O., Shmeltser, E.: Casual analysis of financial and operational risks of oil and gas companies in condition of emergent economy. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2- MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 15. Kiv, A., Semerikov, S., Soloviev, V., Kibalnyk, L., Danylchuk, H., Matviychuk, A.: Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics. In: Kiv, A., Semerikov, S., Soloviev, V., Kibalnyk, L., Danylchuk, H., Matviychuk, A. (eds.) Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019), Odessa, Ukraine, May 22-24, 2019. CEUR Workshop Proceedings 2422, 1–4. http://ceurws.org/Vol-2422/paper00.pdf (2019). Accessed 17 Aug 2020 16. Kryzhanivs'kyi, E., Horal, L., Perevozova, I., Shyiko, V., Mykytiuk, N., Berlous, M.: Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 17. Kucherova, H., Didenko, A., Kravets, O., Honcharenko, Y., Uchitel, A.: Scenario forecasting information transparency of subjects' under uncertainty and development of the knowledge economy. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 18. Larionova, K., Donchenko, T., Oliinyk, A., Kapinos, H., Savenko, O., Barmak, O.: Modeling the assessment of credit risk losses in banking. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEURWS.org, online (2020, in press) 19. Maksyshko, N., Vasylieva, O., Kozin, I., Perepelitsa, V.: Comparative analysis of the attractiveness of investment instruments based on the analysis of market dynamics. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 20. Semerikov, S., Chukharev, S., Sakhno, S., Striuk, A., Osadchyi, V., Solovieva, V., Vakaliuk, T., Nechypurenko, P., Bondarenko, O., Danylchuk, H.: Our sustainable coronavirus future. E3S Web of Conferences 166, 00001 (2020). doi:10.1051/e3sconf/202016600001 21. Shmygol, N., Schiavone, F., Trokhymets, O., Pawliszczy, D., Koval, V., Zavgorodniy, R., Vorfolomeiev, A.: Model for assessing and implementing resource-efficient strategy of industry. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2- MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 22. Soloviev, V., Serdiuk, O., Semerikov, S., Kiv, A.: Recurrence plot-based analysis of financial-economic crashes. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 23. Soloviev, V., Solovieva, V., Tuliakova, A., Hostryk, A., Pichl, L.: Complex networks theory and precursors of financial crashes. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 24. Stadnyk, V., Izhevskiy, P., Khrushch, N., Lysenko, S., Sokoliuk, G., Tomalja, T.: Strategic priorities of innovation and investment development of the Ukraine's economy industrial sector. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) 25. Zelinska, H., Fedorovych, I., Andrusiv, U., Chernova, O., Kupalova, H.: Modeling and prediction of the gas pipelines reliability indicators in the context of energy security of Ukraine. In: Kiv, A. (ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2- MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, CEUR-WS.org, online (2020, in press) | - |
| dc.description.abstract | This is an introductory text to a collection of selected papers from the M3E2 2020 Summer: The Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy, which was held in Odessa, Ukraine, on the July 13-18, 2020. It consists of short introduction and some observations about the event and its future. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | Arnold Kiv | uk_UA |
| dc.subject | machine learning | uk_UA |
| dc.subject | prediction of emergent economy dynamics | uk_UA |
| dc.subject | COVID-19 | uk_UA |
| dc.title | Machine learning of emerging markets in pandemic times | uk_UA |
| dc.type | Article | uk_UA |
| Розташовується у зібраннях: | Кафедра інформатики та прикладної математики | |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| paper00.pdf | article | 9.6 MB | Adobe PDF | Переглянути/Відкрити |
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