Development of a method of data interpretation in intelligent decision support systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.360208

Keywords:

heterogeneous data, processing of various types of data, reliability of decision-making, artificial intelligence

Abstract

Intelligent decision support systems (IDSS) are the object of the study. The problem that is solved in the study is the increase in the processing of heterogeneous data while ensuring the given reliability of their processing. The hypothesis of the study is the possibility of increasing the level of reliability of heterogeneous data processing in IDSS due to the development of a method of data interpretation in IDSS.

The originality of the study consists of:

− taking into account the influence of data uncertainty on the process of processing heterogeneous data in IDSS due to the use of fuzzy analytical expressions;

− reduction of loss of reliability of heterogeneous data processing due to verification of information about IDSS and data circulating in it;

− increasing the reliability of heterogeneous data processing in IDSS due to multi-level deep learning of knowledge bases, using evolving artificial neural networks;

− estimation of zero data values in IDSS databases, due to the use of the procedure for estimating the zero data value, which achieves the prevention of looping of the method;

− carry out unambiguous classification of data, their attributes circulating in the IDSS due to the use of artificial immune detectors, which achieves an increase in the accuracy of IDSS settings and the reliability of heterogeneous data processing;

− recovery of data that was lost during the processing of heterogeneous data in IDSS due to their preliminary processing, which achieves an increase in the reliability of heterogeneous data circulating in IDSS.

The proposed method provides an increase in the efficiency of heterogeneous data processing by increasing the reliability of decision-making at the level of 14−18% due to the use of additional procedures, which is confirmed by the results of a computational experiment

Author Biographies

Andrii Shyshatskyi, Kharkiv National Automobile and Highway University

Doctor of Technical Sciences, Senior Researcher, Professor

Department of Computer Science and Information Systems

Anatolii Pavlikovskyi, National Defence University of Ukraine

Candidate of Military Sciences, Associate Professor, Head of Institute

Institute of Information and Communication Technologies and Cyber Defense

Pavlo Zhuk, National Defence University of Ukraine

Candidate of Technical Sciences, Associate Professor, Head of Institute

Institute of Professional Military Education “Leadership Training”

Oleksii Nalapko, Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine

Doctor of Philosophy (PhD)

Scientific and Organizational Department

Volodymyr Cherneha, National Defence University of Ukraine

Candidate of Technical Sciences, Associate Professor, Head of Department

Department of Information and Analytical Technologies

Institute of Information and Communication Technologies and Cyber Defense

Yurii Artabaiev, Yevhenii Bereznyak Military Academy

Candidate of Technical Sciences, Senior Lecturer

Department of Information Technology

Nadiia Protas, Poltava State Agrarian University

Candidate of Agricultural Sciences, Associate Professor

Department of Information Systems and Technologies

Andrii Veretnov, Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine

Candidate of Technical Sciences, Lead Researcher

Scientific and Research Department

Yevhen Peleshok, Research Institute of Military Intelligence

Candidate of Technical Sciences, Associate Professor, Senior Researcher, Senior Research Fellow

Danylo Pliekhov, Kharkiv National Automobile and Highway University

Assistant

Department of Automation and Computer-Aided Technologies

References

  1. Sova, O., Radzivilov, H., Shyshatskyi, A., Shvets, P., Tkachenko, V., Nevhad, S. et al. (2022). Development of a method to improve the reliability of assessing the condition of the monitoring object in special-purpose information systems. Eastern-European Journal of Enterprise Technologies, 2 (3 (116)), 6–14. https://doi.org/10.15587/1729-4061.2022.254122
  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. Wang, J., Neil, M., Fenton, N. (2020). A Bayesian network approach for cybersecurity risk assessment implementing and extending the FAIR model. Computers & Security, 89, 101659. https://doi.org/10.1016/j.cose.2019.101659
  7. Matheu-García, S. N., Hernández-Ramos, J. L., Skarmeta, A. F., Baldini, G. (2019). Risk-based automated assessment and testing for the cybersecurity certification and labelling of IoT devices. Computer Standards & Interfaces, 62, 64–83. https://doi.org/10.1016/j.csi.2018.08.003
  8. Henriques de Gusmão, A. P., Mendonça Silva, M., Poleto, T., Camara e Silva, L., Cabral Seixas Costa, A. P. (2018). Cybersecurity risk analysis model using fault tree analysis and fuzzy decision theory. International Journal of Information Management, 43, 248–260. https://doi.org/10.1016/j.ijinfomgt.2018.08.008
  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. Promyslov, V. G., Semenkov, K. V., Shumov, A. S. (2019). A Clustering Method of Asset Cybersecurity Classification. IFAC-PapersOnLine, 52 (13), 928–933. https://doi.org/10.1016/j.ifacol.2019.11.313
  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
Development of a method of data interpretation in intelligent decision support systems

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Published

2026-06-26

How to Cite

Shyshatskyi, A., Pavlikovskyi, A., Zhuk, P., Nalapko, O., Cherneha, V., Artabaiev, Y., Protas, N., Veretnov, A., Peleshok, Y., & Pliekhov, D. (2026). Development of a method of data interpretation in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3(4 (141), 66–73. https://doi.org/10.15587/1729-4061.2026.360208

Issue

Vol. 3 No. 4 (141) (2026): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2026 Andrii Shyshatskyi, Anatolii Pavlikovskyi, Pavlo Zhuk, Oleksii Nalapko, Volodymyr Cherneha, Yurii Artabaiev, Nadiia Protas, Andrii Veretnov, Yevhen Peleshok, Danylo Pliekhov

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