Dombi Aggregation Operators for <i>p, qr</i>–Spherica Fuzzy Sets: Application in the Stability Assessment of Cryptocurrencies

Akhtar, Momna; Rahim, Muhammad; Alanzi, Agaeb Mahal; Ahmad, Sadique; Fatlane, Joseph Malose; Aphane, Maggie; Khalifa, Hamiden Abd El-Wahed

doi:10.1109/access.2023.3346916

IEEE Access

2024

DOI: 10.1109/access.2023.3346916

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Dombi Aggregation Operators for p, qr–Spherica Fuzzy Sets: Application in the Stability Assessment of Cryptocurrencies

Momna Akhtar,

Muhammad Rahim,

Agaeb Mahal Alanzi

et al.

Abstract: This paper presents novel operational laws for 𝑝, 𝑞, 𝑟 −spherical fuzzy sets (𝑝, 𝑞, 𝑟 −SFSs) by harnessing the Dombi t-norm (DTN) and t-conorm (DTCN). These laws serve as the foundation for a set of aggregation operators (AOs) designed to consolidate 𝑝, 𝑞, 𝑟 −spherical fuzzy (𝑝, 𝑞, 𝑟 −SF) information. Additionally, a multi-criteria decision-making (MCDM) method is outlined for addressing practical decision-making (DM) challenges. To demonstrate the application of the proposed approach, a numerical … Show more

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Cited by 4 publications

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$$p,q,r-$$Fractional fuzzy sets and their aggregation operators and applications

Gulistan,

Hongbin,

Pedrycz

et al. 2024

Artif Intell Rev

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Using p, q, r− fractional fuzzy sets ( p, q, r− FFS) to demonstrate the stability of cryptocurrencies is considered due to the complex and volatile nature of cryptocurrency markets, where traditional models may fall short in capturing nuances and uncertainties. p, q, r− FFS provides a flexible framework for modeling cryptocurrency stability by accommodating imprecise data, multidimensional analysis of various market factors, and adaptability to the unique characteristics of the cryptocurrency space, potentially offering a more comprehensive understanding of the factors influencing stability. Existing studies have explored Picture Fuzzy Sets and Spherical Fuzzy Sets, built on membership, neutrality, and nonmembership grades. However, these sets can't reach the maximum value (equal to 1 ) due to grade constraints. For example, when considering ℘ = (h, ⟨0.9,0.8,1.0⟩�h ∈ H ) , these sets fall short. This is obvious when a decision-maker possesses complete confidence in an alternative, they have the option to assign a value of 1 as the assessment score for that alternative. This signifies that they harbor no doubts or uncertainties regarding the chosen option. To address this, p, q, r− Fractional Fuzzy Sets ( p, q, r− FFSs) are introduced, using new parameters p , q , and r . These parameters abide by p,q ≥ 1 and r as the least common multiple of p and q . We establish operational laws for p, q, r− FFSs. Based on these operational laws, we proposed a series of aggregation operators (AOs) to aggregate the information in context of p, q, r− fractional fuzzy numbers. Furthermore, we constructed a novel multi-criteria group decision-making (MCGDM) method to deal with real-world decisionmaking problems. A numerical example is provided to demonstrate the proposed approach.

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$$p,q,r-$$Fractional fuzzy sets and their aggregation operators and applications

Gulistan,

Hongbin,

Pedrycz

et al. 2024

Artif Intell Rev

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Correlation coefficient of r,s,t-spherical hesitant fuzzy sets and MCDM problems based on clustering algorithm and technique for order preference by similarity to ideal solution method

ÖZLÜ,

AKTAŞ

2024

Comp. Appl. Math.

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Confidence Levels-Based p, q, r–Spherical Fuzzy Aggregation Operators and Their Application in Selection of Solar Panels

Rahim,

Ahmad,

Bajri

et al. 2024

IEEE Access

Self Cite

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𝑝, 𝑞, 𝑟 −spherical fuzzy sets are an extension of picture fuzzy, spherical fuzzy and t-spherical fuzzy sets, which allow for a more flexible representation of uncertainty. In a 𝑝, 𝑞, 𝑟 −spherical fuzzy set, decision makers have the flexibility to adjust the influence of membership grades simultaneously by integrating parameters 𝑝, 𝑞, and 𝑟, enabling them to manage the impact of these grades effectively. In this setting, while considering the confidence levels associated with each 𝑝, 𝑞, 𝑟 −spherical fuzzy number (𝑝, 𝑞, 𝑟 −SFN), the current study explored novel averaging and geometric operators known as confidence 𝑝, 𝑞, 𝑟 −spherical fuzzy weighted averaging (𝐶 𝑝,𝑞,𝑟 𝑆𝐹𝑊𝐴) and confidence 𝑝, 𝑞, 𝑟 −spherical fuzzy weighted geometric (𝐶 𝑝,𝑞,𝑟 𝑆𝐹𝑊𝐺), along with their associated desirable properties. Subsequently, a multi-criteria decision-making (MCDM) method is proposed and demonstrated through an example related to the selection of best solar panels to validate its soundness and effectiveness. The computed results are then compared with some existing approach to further support the outcomes of the proposed work.

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Dombi Aggregation Operators for p, qr–Spherica Fuzzy Sets: Application in the Stability Assessment of Cryptocurrencies

Cited by 4 publications

References 47 publications

$$p,q,r-$$Fractional fuzzy sets and their aggregation operators and applications

$$p,q,r-$$Fractional fuzzy sets and their aggregation operators and applications

Correlation coefficient of r,s,t-spherical hesitant fuzzy sets and MCDM problems based on clustering algorithm and technique for order preference by similarity to ideal solution method

Confidence Levels-Based p, q, r–Spherical Fuzzy Aggregation Operators and Their Application in Selection of Solar Panels

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