Engineering Review : International journal for publishing of original researches from the aspect of structural analysis, materials and new technologies in the field of mechanical engineering, shipbuilding, fundamental engineering sciences, computer sciences, electrical engin, Vol. 44 No. 4 - SI 2024, 2024.
Original scientific paper
https://doi.org/10.30765/er.2571
Analysis of power aggregation operators through circular intuitionistic fuzzy information and their applications in machine learning analysis
Maleha Rukhsar
orcid.org/0009-0002-2469-3334
; Department of Mathematics, Riphah International University (Lahore Campus), Lahore, Pakistan
Kifayat Ullah
; Department of Mathematics, Riphah International University (Lahore Campus), Lahore, Pakistan
*
Zeeshan Ali
; Department of Information Management, National Yunlin University of Science and Technology Douliu, Yunlin, Taiwan
Abrar Hussain
orcid.org/0000-0003-2289-7464
; Department of Mathematics, Riphah International University (Lahore Campus), Lahore, Pakistan
* Corresponding author.
Abstract
Machine learning language is very valuable for depicting different problems, especially computer language, data mining, data sciences, and machine language. The circular intuitionistic fuzzy set (C-IFS) is a flexible approach to fuzzy sets and intuitionistic fuzzy sets. Keep in mind the flexibility of C-IFS, decision-maker used C-IFS to cope with incomplete and redundant human opinions accurately. Furthermore, power operators are used for depicting or aggregating the collection of data into a singleton set. In this manuscript, we explore the power operators for circular intuitionistic fuzzy (C-IF) information, such as C-IF power weighted averaging (C-IFPWA) operator, C-IF power weighted ordered averaging (C-IFPWOA) operator, C-IF power weighted geometric (C-IFPWG) operator, and C-IF power weighted ordered geometric (C-IFPWOG) operator. Some properties of the above information are also stated. Additionally, we evaluate the procedure of the multi-attribute decision-making (MADM) technique for resolving the utilization of the most suitable part of machine learning in complicated scenarios. Finally, we illustrate some numerical examples for addressing the comparison between proposed techniques and existing methods to show the effectiveness and reliability of the presented operators.
Keywords
circular intuitionistic fuzzy sets; power aggregation operators; machine learning algorithms; decision-making procedures
Hrčak ID:
324611
URI
Publication date:
23.12.2024.
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