Development of a method for assessing the technical characteristics of specialized hierarchical systems using artificial intelligence

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.329094

Keywords:

convolutional artificial neural networks, genetic algorithm, destabilizing factors, metaheuristic algorithm

Abstract

The object of the study is specialized hierarchical systems. The problem addressed in the research is improving the efficiency of evaluating hierarchical systems while ensuring a specified level of reliability regardless of the volume of data entering the system. The originality of the method lies in the use of additional enhanced procedures that allow to:

– verify the topology and parameters of specialized hierarchical systems, considering the degree of uncertainty of the initial data known about them. The consideration of uncertainty is achieved through the application of corresponding correction coefficients;

– perform a primary selection of individuals for tuning the convolutional artificial neural network using an improved genetic algorithm, which reduces solution search time and increases the reliability of obtained results;

– explore the solution spaces of the problem of evaluating the state of specialized hierarchical systems described by atypical functions using an improved monkey swarm algorithm;

– tune the memory of the convolutional artificial neural network via a memory training procedure, thereby reducing the estimation error of parameters of specialized hierarchical systems;

– adjust the weights of the convolutional artificial neural network, which leads to increased accuracy in estimating parameters of specialized hierarchical systems;

– employ additional mechanisms to adjust convolutional artificial neural network parameters by changing the membership function.

An increase in decision-making efficiency of 15–18% was established due to the use of additional procedures, with the reliability of the decisions ensured at the level of 0.9

Author Biographies

Qasim Abbood Mahdi, Al Taff University College

PhD, Head of the Department

Department of Computer Technologies Engineering

Anastasiia Voznytsia, State University “Kyiv Aviation Institute”

PhD Student

Igor Shostak, National Aerospace University Kharkiv Aviation Institute

Doctor of Technical Sciences, Professor

Department of Software Engineering

Andrii Lebedynskyi, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Computer Science and Information Systems

Oleh Ivanenko, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Operation, Testing, Service of Construction and Road Machines

Olena Feoktystova, National Aerospace University Kharkiv Aviation Institute

PhD, Associate Professor

Department of Software Engineering

Vitalii Fedoriienko, National Defence University of Ukraine

PhD, Head of Research Department

Strategic Communications Institute

Nadiia Babkova, National Technical University “Kharkiv Polytechnic Institute”

PhD, Associate Professor, Head of Department

Department of Intelligent Computer Systems

Kostiantin Radchenko, State University “Kyiv Aviation Institute”

PhD Student

Yevhen Karpov, State University “Kyiv Aviation Institute”

PhD Student

References

  1. Bashkyrov, O. M., Kostyna, O. M., Shyshatskyi, A. V. (2015). Rozvytok intehrovanykh system zviazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viyskova tekhnika, 1, 35–39. Available at: http://nbuv.gov.ua/UJRN/ovt_2015_1_7
  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. Shyshatskyi, A. (2020). Complex Methods of Processing Different Data in Intellectual Systems for Decision Support System. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4), 5583–5590. https://doi.org/10.30534/ijatcse/2020/206942020
  7. Yeromina, N., Kurban, V., Mykus, S., Peredrii, O., Voloshchenko, O., Kosenko, V. et al. (2021). The Creation of the Database for Mobile Robots Navigation under the Conditions of Flexible Change of Flight Assignment. International Journal of Emerging Technology and Advanced Engineering, 11 (5), 37–44. https://doi.org/10.46338/ijetae0521_05
  8. Shyshatskyi, A., Stasiuk, T., Odarushchenko, E., Berezanska, K., Demianenko, H. (2023). Method of assessing the state of hierarchical objects based on bio-inspired algorithms. Advanced Information Systems, 7 (3), 44–48. https://doi.org/10.20998/2522-9052.2023.3.06
  9. Ko, Y.-C., Fujita, H. (2019). An evidential analytics for buried information in big data samples: Case study of semiconductor manufacturing. Information Sciences, 486, 190–203. https://doi.org/10.1016/j.ins.2019.01.079
  10. Ramaji, I. J., Memari, A. M. (2018). Interpretation of structural analytical models from the coordination view in building information models. Automation in Construction, 90, 117–133. https://doi.org/10.1016/j.autcon.2018.02.025
  11. Pérez-González, C. J., Colebrook, M., Roda-García, J. L., Rosa-Remedios, C. B. (2019). Developing a data analytics platform to support decision making in emergency and security management. Expert Systems with Applications, 120, 167–184. https://doi.org/10.1016/j.eswa.2018.11.023
  12. Chen, H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, 131, 952–958. https://doi.org/10.1016/j.procs.2018.04.233
  13. Chan, H. K., Sun, X., Chung, S.-H. (2019). When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process? Decision Support Systems, 125, 113114. https://doi.org/10.1016/j.dss.2019.113114
  14. Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. https://doi.org/10.1016/j.future.2018.06.046
  15. Gödri, I., Kardos, C., Pfeiffer, A., Váncza, J. (2019). Data analytics-based decision support workflow for high-mix low-volume production systems. CIRP Annals, 68 (1), 471–474. https://doi.org/10.1016/j.cirp.2019.04.001
  16. Harding, J. L. (2013). Data quality in the integration and analysis of data from multiple sources: some research challenges. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W1, 59–63. https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013
  17. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24 (1), 65–75. https://doi.org/10.1016/s0020-7373(86)80040-2
  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. Kashkevich, S. (Ed.) (2025). Decision support systems: mathematical support. Kharkiv: ТЕСHNOLOGY СЕNTЕR PC, 202. https://doi.org/10.15587/978-617-8360-13-9
  20. Litvinenko, O., Kashkevich, S., Shyshatskyi, A., Dmytriieva, O., Neronov, S., Plekhova, G. et al.; Shyshatskyi, A. (Ed.) (2024). Information and control systems: modelling and optimizations. Kharkiv: TECHNOLOGY CENTER PC, 180. https://doi.org/10.15587/978-617-8360-04-7
Development of a method for assessing the technical characteristics of specialized hierarchical systems using artificial intelligence

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Published

2025-06-30

How to Cite

Mahdi, Q. A., Voznytsia, A., Shostak, I., Lebedynskyi, A., Ivanenko, O., Feoktystova, O., Fedoriienko, V., Babkova, N., Radchenko, K., & Karpov, Y. (2025). Development of a method for assessing the technical characteristics of specialized hierarchical systems using artificial intelligence. Eastern-European Journal of Enterprise Technologies, 3(3 (135), 6–12. https://doi.org/10.15587/1729-4061.2025.329094

Issue

Vol. 3 No. 3 (135) (2025): Control processes

Section

Control processes

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Copyright (c) 2025 Qasim Abbood Mahdi, Anastasiia Voznytsia, Igor Shostak, Andrii Lebedynskyi, Oleh Ivanenko, Olena Feoktystova, Vitalii Fedoriienko, Nadiia Babkova, Kostiantin Radchenko, Yevhen Karpov

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