Creating a polymodel framework for the construction of intelligent decision support systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.356307

Keywords:

system modeling, data representation, decision-making, multidimensionality of data description

Abstract

Intelligent decision support systems (IDSS) are the object of the study. The research problem is to improve the accuracy of the mathematical description of the process of processing heterogeneous data in IDSS. The subject of the study is a mathematical description of the processes of processing heterogeneous data in IDSS. The proposed polymodel complex for the construction of IDSS solutions, which allows:

– systemically represent the relationship between IDSS construction models in the course of their calculation and computing tasks;

– simulate the process of functioning of the IDSS, due to the use of an algebraic (formal) approach to object-oriented modeling and design of the IDSS;

– determine the rational tactical and technical indicators of the IDSS for solving specific calculation and computing tasks, due to the multi-level description of the order of construction of the IDSS;

– make the transition from one type of data representation in IDSS to another due to the presence of appropriate mathematical transformations;

– multidimensional to describe the process of processing heterogeneous data in IDSS, due to the use of a multidimensional matrix model of IDSS data;

– approach the solution of computational-calculation tasks in IDSS by using an interconnected set of mathematical models of IDSS construction;

– formalize the process of constructing IDSS, which allows combining IDSSs using different algorithmic and software. The disadvantages of the proposed polymodel complex include the need to adapt the mathematical apparatus to the specific operating conditions of the IDSS.

The proposed polymodel complex should be used for the construction of IDSS to solve general and specialized calculation tasks, as well as to solve the task of integrating various types of IDSS

Author Biographies

Nina Kuchuk, National Technical University “Kharkiv Polytechnic Institute”

Doctor of Technical Sciences, Professor

Department of Computer Engineering and Programming

Leonid Artushin, State Research Institute of Aviation

Doctor of Technical Sciences, Professor, Chief Researcher

Yurii Zhuravskyi, Zhytomyr Polytechnic State University

Doctor of Technical Sciences, Professor

Department of Computer Technology in Medicine and Telecommunications

Iraida Stanovska, Odesа Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Advanced Mathematics and Systems Modelling

Oleksii Kononov, State Research Institute of Aviation

Doctor of Technical Sciences, Associate Professor, Chief Researcher

Nadiia Protas, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Serhii Shostak, National University of Life and Environmental Sciences of Ukraine

PhD, Associate Professor

Department of Higher and Applied Mathematics

Serhii Neronov, Kharkiv National Automobile and Highway University

PhD, Senior Lecturer

Department of Computer Science and Information Systems

Anton Nikitenko, National Defence University of Ukraine

PhD, Deputy Head of Department

Department of Operational Art

Andrii Veretnov, Central Scientific Research Institute of Armament and Military Equipment of Armed Forces of Ukraine

PhD, Leading Researcher

Research Department

References

  1. Kuchuk, N., Shyshatskyi, A., Radchenko, V., Andrusenko, Y., Klivets, S. (2026). Design of a multilevel architecture for optimizing virtual machine migration. Advanced Information Systems, 10 (2), 35–43. https://doi.org/10.20998/2522-9052.2026.2.04
  2. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et al. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (2 (105)), 37–47. https://doi.org/10.15587/1729-4061.2020.203301
  3. Sova, O., Shyshatskyi, A., Salnikova, O., Zhuk, O., Trotsko, O., Hrokholskyi, Y. (2021). Development of a method for assessment and forecasting of the radio electronic environment. EUREKA: Physics and Engineering, 4, 30–40. https://doi.org/10.21303/2461-4262.2021.001940
  4. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78–89. https://doi.org/10.21303/2461-4262.2020.001353
  5. Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. https://doi.org/10.15587/1729-4061.2020.208554
  6. Li, Z., Xiong, J. (2024). Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization. Energies, 17 (10), 2316. https://doi.org/10.3390/en17102316
  7. Lee, B. M. (2025). Efficient Resource Management for Massive MIMO in High-Density Massive IoT Networks. IEEE Transactions on Mobile Computing, 24 (3), 1963–1980. https://doi.org/10.1109/tmc.2024.3486712
  8. Gasimov, V., Mammadov, J., Islamov, I., Hashimov, E. (2026). Evaluation of alternative solutions for the effective structure of the cyber security system in critical information infrastructures by the hierarchical analysis method. Advanced Information Systems, 10 (2), 87–99. https://doi.org/10.20998/2522-9052.2026.2.10
  9. Folorunso, O., Mustapha, O. A. (2015). A fuzzy expert system to Trust-Based Access Control in crowdsourcing environments. Applied Computing and Informatics, 11 (2), 116–129. https://doi.org/10.1016/j.aci.2014.07.001
  10. Mohammad, A. (2020). Development of the concept of electronic government construction in the conditions of synergetic threats. Technology Audit and Production Reserves, 3 (2 (53)), 42–46. https://doi.org/10.15587/2706-5448.2020.207066
  11. Bodin, L. D., Gordon, L. A., Loeb, M. P., Wang, A. (2018). Cybersecurity insurance and risk-sharing. Journal of Accounting and Public Policy, 37 (6), 527–544. https://doi.org/10.1016/j.jaccpubpol.2018.10.004
  12. Cormier, A., Ng, C. (2020). Integrating cybersecurity in hazard and risk analyses. Journal of Loss Prevention in the Process Industries, 64, 104044. https://doi.org/10.1016/j.jlp.2020.104044
  13. Hoffmann, R., Napiórkowski, J., Protasowicki, T., Stanik, J. (2020). Risk based approach in scope of cybersecurity threats and requirements. Procedia Manufacturing, 44, 655–662. https://doi.org/10.1016/j.promfg.2020.02.243
  14. Perrine, K. A., Levin, M. W., Yahia, C. N., Duell, M., Boyles, S. D. (2019). Implications of traffic signal cybersecurity on potential deliberate traffic disruptions. Transportation Research Part A: Policy and Practice, 120, 58–70. https://doi.org/10.1016/j.tra.2018.12.009
  15. Isong, A., Stephen, B. U.-A., Asuquo, P., Ihemereze, C., Enang, I. (2026). Machine learning based cloud computing intrusion detection. Advanced Information Systems, 10 (1), 115–125. https://doi.org/10.20998/2522-9052.2026.1.13
  16. Zarreh, A., Saygin, C., Wan, H., Lee, Y., Bracho, A. (2018). A game theory based cybersecurity assessment model for advanced manufacturing systems. Procedia Manufacturing, 26, 1255–1264. https://doi.org/10.1016/j.promfg.2018.07.162
  17. Zhuravskyi, Y. (Ed.) (2026). Intelligent decision support systems: methods for optimizing and supporting management decisions. Kharkiv: TECHNOLOGY CENTER PC. https://doi.org/10.15587/978-617-8360-23-8
  18. Koval, M., Sova, O., Shyshatskyi, A., Artabaiev, Y., Garashchuk, N., Yivzhenko, Y. et al. (2022). Improving the method for increasing the efficiency of decision-making based on bio-inspired algorithms. Eastern-European Journal of Enterprise Technologies, 6 (4 (120)), 6–13. https://doi.org/10.15587/1729-4061.2022.268621
  19. Shyshatskyi, A. (Ed.) (2024). Information and control systems: modelling and optimizations. Kharkiv: TECHNOLOGY CENTER PC. https://doi.org/10.15587/978-617-8360-04-7
  20. Voznytsia, A., Sharonova, N., Babenko, V., Ostapchuk, V., Neronov, S., Feoktystov, S. et al. (2025). Development of methods for intelligent assessment of parameters in decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (4 (136)), 73–82. https://doi.org/10.15587/1729-4061.2025.337528
Creating a polymodel framework for the construction of intelligent decision support systems

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Published

2026-04-30

How to Cite

Kuchuk, N., Artushin, L., Zhuravskyi, Y., Stanovska, I., Kononov, O., Protas, N., Shostak, S., Neronov, S., Nikitenko, A., & Veretnov, A. (2026). Creating a polymodel framework for the construction of intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 2(4 (140), 26–38. https://doi.org/10.15587/1729-4061.2026.356307

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Section

Mathematics and Cybernetics - applied aspects