Scientific-method apparatus for improving the efficiency of information processing using artificial intelligence

Authors

Kharkiv National Automobile and Highway University, Ukraine
https://orcid.org/0000-0001-8693-3706
Kharkiv National Automobile and Highway University, Ukraine
https://orcid.org/0000-0003-0758-9244
Military Institute of Taras Shevchenko National University of Kyiv, Ukraine
https://orcid.org/0009-0000-6541-3621
National Technical University “Kharkiv Polytechnic Institute”, Ukraine
https://orcid.org/0000-0002-2080-7173

Keywords:

Bio-inspired algorithms, multi-agent systems, wolf flock algorithm, reliability and adequacy

Synopsis

In this section of the research, a scientific and method apparatus for increasing the efficiency of information processing using artificial intelligence is proposed. The basis of this research is the theory of artificial intelligence, namely evolving artificial neural networks, basic genetic algorithm procedures and bio-inspired algorithms.

During the research, the authors proposed:

– the method of parametric optimization based on the improved wolf flock algorithm;

– the method of parametric evaluation of the control object based on the improved firefly algorithm;

– the method of finding solutions using the improved locust swarm algorithm;

– the method of finding solutions using the improved emperor penguin algorithm.

As a criterion for the efficiency of the specified scientific and method apparatus, the promptness of decision making regarding the parametric control of the state of the object with the given reliability was chosen. This makes it possible to create a hierarchical description of a complex process by levels of generalization and conduct an appropriate analysis of its state. The use of the proposed scientific and method apparatus will allow:

– to reduce the probability of premature convergence of the algorithm;

– to maintain a balance between the convergence speed of the algorithm and diversification;

– to take into account the type of uncertainty and noisy data;

– to take into account the available computing resources of the state analysis system of the analysis object;

– to take into account the priority of search by flock agents;

– to carry out the initial display of individuals taking into account the type of uncertainty;

– to conduct accurate training of AS individuals;

– to conduct a local and global search taking into account the degree of noise of data on the state of the analysis object;

– to apply as a universal tool for solving the task of analyzing the state of analysis objects due to the hierarchical description of analysis objects;

– to check the adequacy of the obtained results;

– to avoid the local extremum problem.

References

Shyshatskyi, A. V., Bashkyrov, O. M., Kostyna, O. M. (2015). Rozvytok intehrovanykh system zviazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viiskova tekhnika, 1 (5), 35–40.

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

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

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

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

Shyshatskyi, A., Zvieriev, O., Salnikova, O., Demchenko, Ye., Trotsko, O., Neroznak, Ye. (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

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

Rotshtein, A. P. (1999). Intellektualnye tekhnologii identifikatcii: nechetkie mnozhestva, geneticheskie algoritmy, neironnye seti. Vinnitca: UNIVERSUM, 320.

Alpeeva, E. A., Volkova, I. I. (2019). The use of fuzzy cognitive maps in the development of an experimental model of automation of production accounting of material flows. Russian Journal of Industrial Economics, 12 (1), 97–106. https://doi.org/10.17073/2072-1633-2019-1-97-106

Zagranovskaia, A. V., Eissner, Iu. N. (2017). Simulation scenarios of the economic situation based on fuzzy cognitive maps. Modern economics: problems and solutions, 10 (94), 33–47. https://doi.org/10.17308/meps.2017.10/1754

Simankov, V. S., Putiato, M. M. (2013). Issledovanie metodov kognitivnogo analiza. Sistemnyi analiz, upravlenie i obrabotka informatcii, 13, 31–35.

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

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

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

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

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

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

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

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

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

Gorelova, G. V. (2013). Cognitive approach to simulation of large systems. Izvestiia IuFU. Tekhnicheskie nauki, 3, 239–250.

Orouskhani, M., Orouskhani, Y., Mansouri, M., Teshnehlab, M. (2013). A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems. International Journal of Information Technology and Computer Science, 5 (11), 32–41. https://doi.org/10.5815/ijitcs.2013.11.04

Koshlan, A., Salnikova, O., Chekhovska, M., Zhyvotovskyi, R., Prokopenko, Y., Hurskyi, T. et al. (2019). Development of an algorithm for complex processing of geospatial data in the special-purpose geoinformation system in conditions of diversity and uncertainty of data. Eastern-European Journal of Enterprise Technologies, 5 (9 (101)), 35–45. https://doi.org/10.15587/1729-4061.2019.180197

Mahdi, Q. A., Shyshatskyi, A., Prokopenko, Y., Ivakhnenko, T., Kupriyenko, D., Golian, V. et al. (2021). Development of estimation and forecasting method in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (9 (111)), 51–62. https://doi.org/10.15587/1729-4061.2021.232718

Emelianov, V. V., Kureichik, V. V., Kureichik, V. M., Emelianov, V. V. (2003). Teoriia i praktika evoliutcionnogo modelirovaniia. Moscow: Fizmatlit, 432.

Gorokhovatsky, V., Stiahlyk, N., Tsarevska, V. (2021). Combination method of accelerated metric data search in image classification problems. Advanced Information Systems, 5 (3), 5–12. https://doi.org/10.20998/2522-9052.2021.3.01

Levashenko, V., Liashenko, O., Kuchuk, H. (2020). Building Decision Support Systems based on Fuzzy Data. Advanced Information Systems, 4 (4), 48–56. https://doi.org/10.20998/2522-9052.2020.4.07

Meleshko, Y., Drieiev, O., Drieieva, H. (2020). Method of identification bot profiles based on neural networks in recommendation systems. Advanced Information Systems, 4 (2), 24–28. https://doi.org/10.20998/2522-9052.2020.2.05

Kuchuk, N., Merlak, V., Skorodelov, V. (2020). A method of reducing access time to poorly structured data. Advanced Information Systems, 4 (1), 97–102. https://doi.org/10.20998/2522-9052.2020.1.14

Shyshatskyi, A., Tiurnikov, M., Suhak, S., Bondar, O., Melnyk, A., Bokhno, T., Lyashenko, A. (2020). Method of assessment of the efficiency of the communication of operational troop grouping system. Advanced Information Systems, 4 (1), 107–112. https://doi.org/10.20998/2522-9052.2020.1.16

Raskin, L., Sira, O. (2016). Method of solving fuzzy problems of mathematical programming. Eastern-European Journal of Enterprise Technologies, 5 (4 (83)), 23–28. https://doi.org/10.15587/1729-4061.2016.81292

Lytvyn, V., Vysotska, V., Pukach, P., Brodyak, O., Ugryn, D. (2017). Development of a method for determining the keywords in the slavic language texts based on the technology of web mining. Eastern-European Journal of Enterprise Technologies, 2 (2 (86)), 14–23. https://doi.org/10.15587/1729-4061.2017.98750

Stepanenko, A., Oliinyk, A., Deineha, L., Zaiko, T. (2018). Development of the method for decomposition of superpositions of unknown pulsed signals using the secondorder adaptive spectral analysis. Eastern-European Journal of Enterprise Technologies, 2 (9 (92)), 48–54. https://doi.org/10.15587/1729-4061.2018.126578

Gorbenko, I., Ponomar, V. (2017). Examining a possibility to use and the benefits of post-quantum algorithms dependent on the conditions of their application. Eastern-European Journal of Enterprise Technologies, 2 (9 (86)), 21–32. https://doi.org/10.15587/1729-4061.2017.96321

Lovska, A. A. (2015). Peculiarities of computer modeling of strength of body bearing construction of gondola car during transportation by ferry-bridge. Metallurgical and Mining Industry, 1, 49–54.

Lovska, A., Fomin, O. (2020). A New fastener to ensure the reliability of a passenger car body on a train ferry. Acta Polytechnica, 60 (6), 478–485. https://doi.org/10.14311/ap.2020.60.0478

Koval, M., Sova, O., Orlov, O., Shyshatskyi, A., Artabaiev, Y., Shknai, O., Veretnov, A., Koshlan, O., Zhyvylo, Y., Zhyvylo, I. (2022). Improvement of complex resource management of special-purpose communication systems. Eastern-European Journal of Enterprise Technologies, 5 (9 (119)), 34–44. https://doi.org/10.15587/1729-4061.2022.266009

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August 16, 2024

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How to Cite

Kashkevich, S., Dmytriiev, I., Shevchenko, I., Lytvynenko, O., Shabanova-Kushnarenko, L., & Apenko, N. (2024). Scientific-method apparatus for improving the efficiency of information processing using artificial intelligence. In A. Shyshatskyi (Ed.), INFORMATION AND CONTROL SYSTEMS: MODELLING AND OPTIMIZATIONS (pp. 138–167). Kharkiv: TECHNOLOGY CENTER PC. https://doi.org/10.15587/978-617-8360-04-7.ch5