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Intelligent System in the Context of Business Process Modelling

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article deals with the features and characteristics of intelligent systems for modelling business processes. Their classification was made and criteria for comparison were developed. According to the comparative analysis of existing expert systems for intelligent analysis, a reasonable choice of system for modelling business processes of a particular enterprise has been carried out. In general, it was found that the introduction of intelligent systems for modelling business processes of the enterprise and forecasting its activities for future allows management of the company to obtain relevant and necessary information for the adoption of effective management decisions and the development of a strategic plan.
Rocznik
Strony
163--168
Opis fizyczny
Bibliogr. 15 poz., schem., tab., wykr.
Twórcy
  • Vinnytsia Institute of Trade and Economics of Kyiv National University of Trade and Economics
  • Vinnytsia Institute of Trade and Economics of Kyiv National University of Trade and Economics
  • National University of Water and Environmental Engineering, Rivne, Ukraine
  • Lublin University of Technology, Lublin, Poland
  • Taraz State University after M.Kh.Dulaty, Kazakhstan
  • Taraz State University after M.Kh.Dulaty, Taraz, Kazakhstan
Bibliografia
  • [1] B. V. Kuzmenko, and O. A. Chaikovska, “The systems of artificial intelligence,” Kyiv, Alterpres, 2006.
  • [2] H. F. Ivanchenko, “The systems of artificial intelligence,” Kyiv, KNEU, 2011.
  • [3] D. F. Liuher, “The artificial intelligence: strategies and methods of solving difficult issues,” Moscow, Vyliams (in Russian), 2003, pp. 866.
  • [4] A. A. Emelianov, E. A. Vlasova, and R. V. Duma, “Simulation modeling of economic systems,” Moscow, Finansyiistatistika (in Russian), 2002.
  • [5] D. Waterman, “Guide to expert systems,” Moscow, Myr, 1989.
  • [6] A. A. Barsehian, M. S. Kupryianov, V. V. Stepanenko, and Y. Y. Kholod, “Methods and models of data analysis: OLAP and Data Mining,” St-Petersburg, BKhV, 2004.
  • [7] V. Mashkov and A. Smolarz and V. Lytvynenko, “The problem of system fault-tolerance,” Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska (IAPGOŚ), 4(4), pp. 41-44, 2014.
  • [8] Z. Omiotek and W. Wójcik, “The use of Hellwig's method for dimension reduction in feature space of thyroid ultrasound images,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 4(3), pp. 14-17, 2014.
  • [9] P. V. Poliakov, and S. A. Korobov, “Software tools for developing business plans: Project Expert system,” Volhohrad, vol. HU, pp. 48, 2004.
  • [10] H. S. Prokudin, M. T. Dekhtiaru, “Simulation modeling in informational systems,” Kyiv:NTU., no. 9, pp. 181–189, 2004.
  • [11] A. P. Rotshtein, and H. B. Rakytyanska, “Diagnosis problem solving using fuzzy relations,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 3, pp. 664-675, 2008.
  • [12] S. I. Vyatkin, A. N. Romanyuk, and Z. Y. Gotra, “Offsetting, relations, and blending with perturbation functions,” Proc. of SPIE 10445, 2017.
  • [13] L. I. Timchenko, S. V. Pavlov, N. I. Kokryatskaya, et al. “Bio-inspired approach to multistage image processing,” Proc. of SPIE 10445, 2017.
  • [14] M. F. Kirichenko, Yu. V. Krak, A. A. Polishchuk, “Pseudo inverse and projective matrices in problems of synthesis of functional transformers,” Kibernetika i Sistemnyj Analiz, vol. 40, no. 3, pp. 116-129, 2004.
  • [15] K. G. Selivanova, O. G. Avrunin and S. M. Zlepko, “Quality improvement of diagnosis of the electromyography data based on statistical characteristics of the measured signals, ”Proc. of SPIE 10031, 2016.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d62aaf31-4d82-4b0e-b311-41e5e3d31cf4
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