Intuitionistic fuzzy parameterized fuzzy soft set theory and its application

Joshi, Bhagawati Prasad; Kumar, Abhay; Singh, Akhilesh Kumar; Bhatt, Pradeep Kumar; Bharti, Bhupender Kumar

doi:10.3233/jifs-169805

Cited by 32 publications

(6 citation statements)

References 11 publications

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“…To cope with such situations, Zadeh introduced fuzzy sets and which was further extended by Atanassov by introducing the concept of intuitionistic fuzzy set (IFS), which is characterized in such a way that the sum of the support for membership and support against membership is less than or equal to one. Due to this characteristic, IFS theory is one of the successful and powerful tools to deal with imprecise, vague and ambiguous information, and receives attention to many practitioners to deal with real life situations. But, the aggregation of all the performances in dealing with real life problems is a very critical step to obtain decisions.…”

Section: Introductionmentioning

confidence: 99%

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Joshi¹,

Gegov

2019

Int J Intell Syst

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The concept of q-rung orthopair fuzzy set (q-ROFS) is the extension of intuitionistic fuzzy set (IFS) in which the sum of the qth power of the support for and the qth power of the support against is bounded by one. Therefore, the q-ROFSs are an important way to express uncertain information in broader space, and they are superior to the IFSs and the Pythagorean fuzzy sets. In this paper, the familiarity degree of the experts with the evaluated objects is incorporated to the initial assessments under q-rung orthopair fuzzy environment. For this, some aggregation operators are proposed to combine these two types of information. Their some important properties are also well proved. Furthermore, these developed operators are utilized in a multicriteria decision-making approach and demonstrated with a real life problem of customers' choice. Then, the experimental results are compared with other existing methods to show its superiority over recent research works.

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Section: Introductionmentioning

confidence: 99%

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Joshi¹,

Gegov

2019

Int J Intell Syst

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“…e following discussion shows the generalization of fpnhs-set as it fulfills all the characteristics, features, and properties of many existing soft set-like models. In Definition 18: [43] fpfs-set ✔ × × ✔ × Deli and Çagman [44] ifps-set ✔ ✔ × ✔ × Joshi et al [47] ifpfs-set ✔ ✔ × ✔ × Karaaslan [48] ifpifs-set ✔ ✔ × ✔ × Riaz and Hashmi [49] fpfs-set ✔ × × ✔ × Zhu and Zhan [50] fpfs Computational Intelligence and Neuroscience similarity measures, like cosine similarity, cotangent similarity, and Dice similarity, and entropy measures. [51].…”

Section: Discussionmentioning

confidence: 99%

“…In Figure 1, the difference between s-set and hs-set is presented with the help of an example for product selection through decisionmaking. (4) Motivated by the above-mentioned literature in general and [43,44,[47][48][49][50] in specific, we construct novel structures of fuzzy parameterized intuitionistic fuzzy hypersoft set (fpifhs-set) and fuzzy parameterized neutrosophic hypersoft set (fpnhs-set) and characterize them with the help of algorithm-based decision-support systems.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

“…(1) e structures like those in [43,44,[47][48][49][50] are made capable with the entitlement of maa-function by characterizing fpifhs-sets and fpnhs-sets. (2) e situations with sub-parametric-valued sets are managed for the environments of if-set and n-set by using fpifhs-sets and fpnhs-sets, (3) Some essential rudiments, i.e., properties, elementary laws, and aggregation operations of fpifhs-set and fpnhs-set are investigated.…”

Section: Main Contributions Main Contributions Of This Research Are G...mentioning

confidence: 99%

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An Intelligent Multiattribute Decision-Support Framework Based on Parameterization of Neutrosophic Hypersoft Set

Rahman

Saeed

Alburaikan

et al. 2022

Computational Intelligence and Neuroscience

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Hypersoft set is a novel area of interest which is able to tackle the real-world scenarios where classification of parameters into their respective sub-parametric values in the form of overlapping sets is mandatory. It employs a new approximate mapping which considers such sets in the form of sub-parametric tuples as its domain. The existing soft set-like structures are insufficient to tackle such kind of situations. This research intends to establish a novel concept of parameterization of fuzzy set under hypersoft set environment with uncertain components of intuitionistic fuzzy set and neutrosophic set. Two novel structures, i.e., fuzzy parameterized intuitionistic fuzzy hypersoft set ( fpifhs -set) and fuzzy parameterized neutrosophic hypersoft set ( fpnhs -set), are developed by employing algebraic techniques like theoretic, analytical, pictorial, and algorithmic techniques. After characterizing the elementary properties and set-theoretic operations of fpifhs -set and fpnhs -set, two novel algorithms are proposed to solve real-life decision-making COVID-19 problem. The results of both algorithms are compared with related already established models through certain evaluating features to judge the advantageous aspects of the proposed study. The generalization of the proposed models is discussed by describing some of their particular cases.

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“…In [31], a nature-inspired metaheuristic algorithm -moth flame optimization -is used for the generation of test sequences in state based testing. In [32], the authors discuss intuitionistic fuzzy parameterized fuzzy soft set (IFP-FS set) and its application in decision making problems. In [33], the authors introduce Interval Valued q-Rung Orthopair Fuzzy Sets (IVqROFSs) and present some of its important operations such as negation, union and intersection.…”

mentioning

confidence: 99%

Special issue on intelligent tools and techniques for signals, machines and automation

Srivastava

Malik

Sharma

2018

IFS

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Intuitionistic fuzzy parameterized fuzzy soft set theory and its application

Cited by 32 publications

References 11 publications

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

An Intelligent Multiattribute Decision-Support Framework Based on Parameterization of Neutrosophic Hypersoft Set

Special issue on intelligent tools and techniques for signals, machines and automation

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