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Engineering Review : Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva, Vol. 44 No. 4 - SI 2024, 2024.

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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 ; 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 ; Department of Mathematics, Riphah International University (Lahore Campus), Lahore, Pakistan

* Dopisni autor.

Puni tekst: engleski pdf 1.028 Kb

str. 141-159

preuzimanja: 0

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Sažetak

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.

Ključne riječi

circular intuitionistic fuzzy sets; power aggregation operators; machine learning algorithms; decision-making procedures

Hrčak ID:

324611

URI

https://hrcak.srce.hr/324611

Datum izdavanja:

23.12.2024.

Posjeta: 0 *