Analysis of Cryptocurrency Market by Using q‐Rung Orthopair Fuzzy Hypersoft Set Algorithm Based on Aggregation Operators

Abstract

One of the most important innovations brought by digitization is the cryptocurrency, also called virtual or digital currency, which has been discussed in recent years and in particular is a new platform for investors. Different types of cryptocurrencies such as Bitcoin, Ethereum, Binance Coin, and Tether do not depend on a central authority. Decision making is complicated by categorization and transmission of uncertainty, as well as verification of digital currency. The weighted average and weighted geometric aggregation operators are used in this article to define a multi-attribute decision-making approach. This work investigates the uniqueness of q-rung orthopair fuzzy hypersoft sets (q-ROFHSS), which respond to instabilities, uncertainty, ambiguity, and imprecise information. This research also covers some fundamental topics of q-ROFHSS. The model offered here is the best option for learning about electronic currency. This study validates the complexity of decision-making problems with different attributes and subattributes to obtain an optimal choice. We conclude that Bitcoin has a diverse set of applications and that crypto assets are well positioned to become an important asset class in decision making.

q-Rung orthopair fuzzy hypersoft ordered aggregation operators and their application towards green supplier

“…So the proposed approach full all these situations. The aggregation operators created by Khan et al, (2022a) can expertly compress with the multiple sub-attributes of the alternatives. However, these aggregation operators may sometimes not produce certain undesirable results.…”

“…Definition 4.6. (Khan et al, 2022a) A weighted geometric q-rung orthopair fuzzy hypersoft (q-ROFHWG) operator is defined as:…”

“…The structure of q-rung orthopair fuzzy hypersoft set have the ability to overcome this difficulties.Research gap: Khan et al, (2022b) introduced the theme of q-rung orthopair fuzzy hypersoft sets with certain fundamental operational laws and aggregation operators, which can handle the various interactions between input arguments. Khan et al, (2022a) then suggested aggregation operators based on operational laws, such as weighted average and weighted geometric. To our knowledge, there are no studies on ordered aggregation operators in the literature.…”

Experimental Heat Transfer Performance of Plate Fin Heat Exchanger Based on Fuzzy Algorithm

“…The instrument device that converts the dynamic flow sensor thermometer into an inductive flow or temperature signal when measuring temperature changes is called a static heat exchanger. According to different installation locations, there can be two forms of flow models, namely, a fixed range flow model and a reverse range fluid model [11][12]. The heat transfer process of plate fin regenerator is to transfer the air flow, convection and radiation to the fluid flow state, and the velocity and momentum under different forms of motion state to the medium.…”

Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets: An Application in Real Estate Project

“…For a complex q-rung orthopair fuzzy (Cq-ROF) environment, Garg et al [26] developed power aggregation operations. The q-rung orthopair fuzzy hypersoft weighted average (q-ROFHWA) and q-rung orthopair fuzzy hypersoft weighted geometric (q-ROFHWG) aggregation operators were developed by Khan et al [27] for use in decision-making problems.…”