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Selecting the foremost big data tool to optimize YouTube data in dynamic Fermatean fuzzy knowledge.

Dilshad Alghazzawi1, Abdul Razaq2, Hanan Alolaiyan3

  • 1Department of Mathematics, College of Science & Arts, King Abdulaziz University, Rabigh, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces novel Fermatean fuzzy dynamic aggregation operators for big data analytics. These methods enhance decision-making by analyzing complex YouTube data effectively.

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Area of Science:

  • Information Science
  • Decision Science

Background:

  • Big data requires advanced tools for analysis and decision support.
  • Fermatean fuzzy set theory effectively handles imprecise and complex information.

Purpose of the Study:

  • To introduce and investigate novel Fermatean fuzzy dynamic ordered weighted aggregation operators.
  • To apply these operators to big data analytics, specifically for YouTube data.

Main Methods:

  • Development of Fermatean fuzzy dynamic ordered weighted averaging and geometric operators.
  • Formulation of a step-by-step mathematical algorithm for decision-making.
  • Application to a big data analytics platform selection for YouTube.

Main Results:

  • The proposed operators demonstrate robust capabilities in handling dynamic and imprecise data.
  • The study successfully identified an effective big data analytics platform for YouTube.
  • Comparative analysis validated the superiority of the novel approaches.

Conclusions:

  • The novel Fermatean fuzzy dynamic aggregation operators offer a powerful tool for big data analysis.
  • These methods provide a structured approach to complex decision-making problems.
  • The research contributes to advancements in fuzzy set theory applications for big data.