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Artificial intelligence components and fuzzy regulators in entrepreneurship development

Author

Listed:
  • Sergii Bogachov

    (PO "Institute for Local and Regional Development", Ukraine)

  • Aleksy Kwilinski

    (London Academy of Science and Business, United Kingdom)

  • Boris Miethlich

    (Comenius University in Bratislava, Slovakia)

  • Viera Bartosova

    (University of Žilina, Slovakia)

  • Aleksandr Gurnak

    (Financial University under the Government of the Russian Federation, Russian Federation)

Abstract

The article provides a comparative study of the possibility of entrepreneurship development based on fuzzy signals of business activity and applied elements of artificial intelligence. The principal research methods that determine the logic and practical basis of the application of fuzzy logic in entrepreneurship are highlighted. It has been determined that fuzzy modeling is effective when technological processes are too complex for analysis using generally accepted quantitative methods, or when available sources of information in the business environment are interpreted poorly, inaccurately, and indefinitely. It has been shown experimentally that fuzzy logic gives better results compared to those obtained with generally accepted algorithms for analyzing the quality of doing business. A model of a neuro-fuzzy regulator has been developed and measures for its implementation in the business environment have been proposed. A neural network model in entrepreneurial development has been formed. Studies have shown the possibility of effective use of the principles of artificial intelligence and modeling in solving problems of developing entrepreneurial potential and making business decisions under conditions of uncertainty. This ensures objective and well-grounded decision-making in solving various applied problems of business development and taking into account environmental factors. The applied tasks of supporting the adoption of entrepreneurial decisions in the conditions are formulated; uncertainty; indicating that approaches to decision-making under conditions of uncertainty based on artificial intelligence and fuzzy logic tools are universal and require appropriate careful study and adaptation to a specific applied problem in the business environment.

Suggested Citation

  • Sergii Bogachov & Aleksy Kwilinski & Boris Miethlich & Viera Bartosova & Aleksandr Gurnak, 2020. "Artificial intelligence components and fuzzy regulators in entrepreneurship development," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 487-499, December.
    • Handle: RePEc:ssi:jouesi:v:8:y:2020:i:2:p:487-499
    DOI: 10.9770/jesi.2020.8.2(29)
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    References listed on IDEAS

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    1. Igors Skute, 2019. "Opening the black box of academic entrepreneurship: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 237-265, July.
    2. Charles C. Snow & Øystein Devik Fjeldstad & Arthur M. Langer, 2017. "Designing the digital organization," Journal of Organization Design, Springer;Organizational Design Community, vol. 6(1), pages 1-13, December.
    3. Nicholas C. Petruzzi & Maqbool Dada, 2002. "Dynamic pricing and inventory control with learning," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(3), pages 303-325, April.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Radoslaw Miskiewicz, 2022. "Clean and Affordable Energy within Sustainable Development Goals: The Role of Governance Digitalization," Energies, MDPI, vol. 15(24), pages 1-17, December.
    2. Huishui Su & Yu Lu & Oleksii Lyulyov & Tetyana Pimonenko, 2023. "Good Governance within Public Participation and National Audit for Reducing Corruption," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
    3. Guangzhen Zhang & Wangyang Jiang, 2023. "Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-16, January.
    4. Radosław Miśkiewicz, 2021. "The Impact of Innovation and Information Technology on Greenhouse Gas Emissions: A Case of the Visegrád Countries," JRFM, MDPI, vol. 14(2), pages 1-10, February.
    5. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    6. Radosław Miśkiewicz & Krzysztof Matan & Jakub Karnowski, 2022. "The Role of Crypto Trading in the Economy, Renewable Energy Consumption and Ecological Degradation," Energies, MDPI, vol. 15(10), pages 1-15, May.
    7. Oleksandr Melnychenko, 2021. "Energy Losses Due to Imperfect Payment Infrastructure and Payment Instruments," Energies, MDPI, vol. 14(24), pages 1-20, December.
    8. Liping Wu & Kai Hu & Oleksii Lyulyov & Tetyana Pimonenko & Ishfaq Hamid, 2022. "The Impact of Government Subsidies on Technological Innovation in Agribusiness: The Case for China," Sustainability, MDPI, vol. 14(21), pages 1-15, October.
    9. Henryk Dzwigol, 2022. "Research Methodology in Management Science: Triangulation," Virtual Economics, The London Academy of Science and Business, vol. 5(1), pages 78-93, April.

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    More about this item

    Keywords

    entrepreneurship; neural network; regulators; linguistic rule; genetic algorithm; object of control;
    All these keywords.

    JEL classification:

    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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