Articles

Methodology of comprehensive diagnostics of technical educational and scientific cluster management risks

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №1 2025

Last Updated on 25 February 2025

Published on 30 November -0001

Hits: 55

Authors:

S.P.Bazhan, orcid.org/0000-0002-5739-4616, Ukrainian State University of Science and Technologies SHEI “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

L.D.Harmider*, orcid.org/0000-0001-7837-2734, Ukrainian State University of Science and Technologies SHEI “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

L.I.Korotka, orcid.org/0000-0003-0780-7571, Ukrainian State University of Science and Technologies SHEI “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.I.Kryvoruchko, orcid.org/0009-0004-2833-3496, FG “Tetyana”, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (1): 132 - 139

https://doi.org/10.33271/nvngu/2025-1/132

Abstract:

Purpose. Substantiation of the methodological approach to assessing the indispensable dangers of managing a technical educational and scientific cluster based on utilizing a fuzzy rationale instrument in order to distinguish issues within the educational cluster and give suitable suggestions for their solution.

Methodology. The methodological premise of the research is the classical provisions and fundamental works by foreign and domestic scientists, statistical data, and the results of unique investigation on the issues of surveying the dangers of cluster improvement. The research utilized the method of fuzzy set theory, comparative analysis, method of abstractions, generalization of the results of advanced theoretical research, a systematic and comprehensive approach. 

Findings. A methodological approach to surveying the dangers of managing a technical educational and scientific cluster was proposed, various tests were carried out based on the official statistics. The analysis of the results of evaluating the main components that impact the performance of the technical educational and scientific cluster made it possible to distinguish issues within the functioning of the cluster and to identify ways to solve them.

Originality. The methodological approach to the evaluation of integral dangers based on the device of fuzzy rationale has been progressed, which, unlike the existing ones, permits you to coordinate strategies of the theory of fuzzy sets and fuzzy rationale and permits you to achieve more exact and important outcomes compared to the conventional strategies. The proposed approach can be utilized in different areas where processing of expansive volumes of fuzzy data is required.

Practical value. The practical significance of the study arises from the possibility of applying the developed fuzzy model for making managerial decisions in a technical educational and scientific cluster under conditions of uncertainty.

Keywords: educational and scientific cluster, risks, fuzzy sets, uncertainty factors, Mamdani method

References.

1. Danchenko, O. B., & Zanora, V. O. (2019). Project management: managing risks and changes in management decision-making processes: monograph. Cherkasy: PE Chabanenko Yu. A.

2. Ishchenko, I. S. (2018). Risks of investment projects. Scientific Bulletin of the Poltava University of Economics and Trade, 5(90), 91-98.

3. Kaleniuk, I., & Kuklin, O. (2018). Risk management in the system of higher education of Ukraine. Bulletin of Taras Shevchenko Kyiv National University, 5(170), 23-28. https://doi.org/10.17721/1728-2667.2015/170-5/4.

4. Lagunova, I. A. (2018). The essence and principles of the concept of risk management. Actual problems of public administration, 1(53), 44-52.

5. Tkachuk, L., & Tkachuk, M. (2022). Peculiarities of managing an educational institution on the basis of risk management. Modern engineering and innovative technologies, 22, 80-87. https://doi.org/10.30890/2567-5273.2022-22-02-045.

6. Ladik, S. H., & Bazilyuk, K. F. (2022). Using the apparatus of fuzzy multiplier theory for estimating the cost of a tourism product. Innovations and technologies in the service sphere and food industry, 1(5), 47-51. https://doi.org/10.32782/2708-4949.1(5).2022.9.

7. Bedriy, D. I., & Polshakov, I.V. (2012). Research projects budgeting with an allowance for risks. Eastern-European Journal of Enterprise Technologies, 1, 12(55), 47-49. https://doi.org/10.15587/1729-4061.2012.3626.

8. Li, H. (2022). Analysis and design of financial data mining system based on fuzzy clustering. Expert Systems, 41(5), 54-63. https://doi.org/10.1111/exsy.13031.

9. Salas-Velasco, M. (2024). Vocational education and training systems in Europe: A cluster analysis. European Educational Research Journal, 23(3), 434-449. https://doi.org/10.1177/14749041221151189.

10. Liu, P., Xu, Y., Li, Y., & Geng, Y. (2024). An FMEA Risk Assessment Method Based on Social Networks Considering Expert Clustering and Risk Attitudes. IEEE Transactions on Engineering Management, 71, 10783-10796. https://doi.org/10.1109/TEM.2024.3402949.

11. Siami, M., Naderpour, M., Ramezani, F., & Lu, J. (2024). Risk Assessment Through Big Data: An Autonomous Fuzzy Decision Support System. IEEE Transactions on Intelligent Transportation Systems, 25(8), 9016-9027. https://doi.org/10.1109/TITS.2024.3392959.

12. Kotsyuba, O. S. (2019). Development of a fuzzy-multiple risk measurement apparatus: the case of simultaneous vagueness of the criterion indicator and its standard. Problems of economics, 4(42), 264-271.

13. Harmider, L. D., Korotka, L. I., Bazhan, S. P., & Aniske­vich, D. M. (2024). The application of fuzzy sets theory in the methodological approach to assessing personnel risks of an enterprise. Academy review, 1(60), 192-205. https://doi.org/10.32342/2074-5354-2024-1-60-14.

14. Zelentsov, D. G., & Korotkaya, L. I. (2018). Technologies of Computational Intelligence in Tasks of Dynamic Systems Modeling: monograph. Dnipro: Balans-Klub. https://doi.org/10.32434/mono-1-ZDG-KLI.

15. Wang, Q., Liu, L., Liu, Z., Qiao, W., Liu, F., & Abbas, S. (2022). Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment.  Mathematical Problems in Engineering. https://doi.org/10.1155/2022/3438666.

16. Shtovba, S., & Shtovba, E. (2013) A citation index with allowance for the implicit diffusion of scientific knowledge. Scientific and Technical Information Processing, 40(3), 142-145. https://doi.org/10.3103/S0147688213030040.

17. Chen, T., Shang, C., Su, P., & Shen, Q. (2018). Induction of accurate and interpretable fuzzy rules from preliminary crisp representation. Knowledge-Based Systems, 146, 152-166. https://doi.org/10.1016/j.knosys.2018.02.003.

18. Gegov, A., Sanders, D., & Vatchova, B. (2017). Aggregation of inconsistent rules for fuzzy rule base simplification. International Journal of Knowledge-Based and Intelligent Engineering Systems, 21(3), 135-145. https://doi.org/10.3233/KES-170358.

19. Shtovba, S., & Shtovba, O. (2013). Simple rational extension of Hirsch index. Sociology of Science and Technology, 4(4), 99-103.

20. Hilletofth, P., Sequeira, M., & Adlemo, A. (2019). Three novel fuzzy logic concepts applied to reshoring decision-making. Expert Systems with Applications, 126, 133-143. https://doi.org/10.1016/j.eswa.2019.02.018.

21. Korotka, L. I. (2021). Certificate of registration of copyright for work No. 105604 “Computer program “Programs for tools for designing fuzzy systems of the Mamdani type”. Bulletin Copyright and proprietary rights, 65, 496. Retrieved from https://ukrpatent.org/uk/articles/bulletin-copyright.

22. Cordón, O. (2011). A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. International Journal of Approximate Reasoning, 52(6), 894-913. https://doi.org/10.1016/j.ijar.2011.03.004.

23. Mencar, C., Castiello, C., Cannone, R., & Fanelli, A. M. (2011). Interpretability assessment of fuzzy knowledge bases – a cointension based approach. International Journal of Approximate Reasoning, 52(4), 501-518. https://doi.org/10.1016/j.ijar.2010.11.007.

24. Duţu, L. C., Mauris, G., & Bolon, P. (2018). A fast and accurate rule-base generation method for Mamdani fuzzy systems. IEEE Transactions on Fuzzy Systems, 26(2), 715-733. https://doi.org/10.1109/TFUZZ.2017.2688349.

25. Parpiyeva, A., & Duisenov, N. (2022). Ensuring the Accuracy and Transparency of the Mamdani Fuzzy Model when Training Experimental Data. International Journal of Innovative Research in Science Engineering and Technology, 11(2), 1664-1675. https://doi.org/10.15680/IJIRSET.2022.1102120.

26. Ternovoy, M. Yu., & Shtogrina, E. S. (2015). Formal specification of properties of Mamdani fuzzy knowledge bases based on a metagraph. Bulletin of Kharkiv National University named after V.N. Karazina, 157-171.

• < Prev
• Next >