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Impact of Machine Learning and Artificial Intelligence in Business Based on Intuitionistic Fuzzy Soft WASPAS Method

Author

Listed:
  • Majed Albaity

    (Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80348, Jeddah 22254, Saudi Arabia)

  • Tahir Mahmood

    (Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Zeeshan Ali

    (Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad 44000, Pakistan
    KERMIT, Department of Mathematical Modeling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium)

Abstract

Artificial intelligence (AI) is a well-known and reliable technology that enables a machine to simulate human behavior. While the major theme of AI is to make a smart computer system that thinks like a human to solve awkward problems, machine learning allows a machine to automatically learn from past information without the need for explicit programming. In this analysis, we aim to derive the idea of Aczel–Alsina aggregation operators based on an intuitionistic fuzzy soft set. The initial stage was the discovery of the primary and critical Aczel–Alsina operational laws for intuitionistic fuzzy soft sets. Subsequently, we pioneer a range of applicable theories (set out below) and identify their essential characteristics and key results: intuitionistic fuzzy soft Aczel–Alsina weighted averaging; intuitionistic fuzzy soft Aczel–Alsina ordered weighted averaging; intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators; and intuitionistic fuzzy soft Aczel–Alsina ordered weighted geometric operators. Additionally, by utilizing certain key information, including intuitionistic fuzzy soft Aczel–Alsina weighted averaging and intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators, we also introduce the theory of the weighted aggregates sum product assessment method for intuitionistic fuzzy soft information. This paper also introduces a multi-attribute decision-making method, which is based on derived operators for intuitionistic fuzzy soft numbers and seeks to assess specific industrial problems using artificial intelligence or machine learning. Finally, to underline the value and reasonableness of the information described herein, we compare our obtained results with some pre-existing information in the field. This comparison is supported by a range of numerical examples to demonstrate the practicality of the invented theory.

Suggested Citation

  • Majed Albaity & Tahir Mahmood & Zeeshan Ali, 2023. "Impact of Machine Learning and Artificial Intelligence in Business Based on Intuitionistic Fuzzy Soft WASPAS Method," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1453-:d:1099652
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    References listed on IDEAS

    as
    1. Zeeshan Ali & Tahir Mahmood & Miin-Shen Yang, 2020. "TOPSIS Method Based on Complex Spherical Fuzzy Sets with Bonferroni Mean Operators," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    2. Tahir Mahmood & Ali Jaballah, 2020. "A Novel Approach towards Bipolar Soft Sets and Their Applications," Journal of Mathematics, Hindawi, vol. 2020, pages 1-11, October.
    3. Harish Garg & Rishu Arora, 2018. "Bonferroni mean aggregation operators under intuitionistic fuzzy soft set environment and their applications to decision-making," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1711-1724, November.
    4. Muhammad Akram & Maria Shabir & Ahmad N. Al-Kenani & José Carlos R. Alcantud & Sami Ullah Khan, 2021. "Hybrid Decision-Making Frameworks under Complex Spherical Fuzzy N-Soft Sets," Journal of Mathematics, Hindawi, vol. 2021, pages 1-46, March.
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    Cited by:

    1. Wajid Ali & Tanzeela Shaheen & Hamza Ghazanfar Toor & Faraz Akram & Md. Zia Uddin & Mohammad Mehedi Hassan, 2023. "An Innovative Decision Model Utilizing Intuitionistic Hesitant Fuzzy Aczel-Alsina Aggregation Operators and Its Application," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    2. Georgia Irina Oros & Simona Dzitac & Daniela Andrada Bardac-Vlada, 2024. "Introducing the Third-Order Fuzzy Superordination Concept and Related Results," Mathematics, MDPI, vol. 12(19), pages 1-12, October.

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