Pythagorean m-polar fuzzy sets and TOPSIS method for the selection of advertisement mode

Naeem, Khalid; Riaz, Muhammad; Afzal, Deeba

doi:10.3233/jifs-191087

Cited by 57 publications

(31 citation statements)

References 50 publications

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“…We recall some fundamentals of Pythagorean m-polar fuzzy sets and their operational laws accompanied by operational laws of corresponding numbers in this segment. Definition 1 (see [11]). A Pythagorean m-polar fuzzy set (PmFS) O is characterized by two sets of mappings Υ (i) O (denoting affiliation degrees) and o � (i) O (meant for dissociation grades) dropping members of X to [0, 1] constrained to obey 0 ≤ (Υ (i) O (g) 2 is known as hesitation margin or indeterminacy degree of g ∈ X to O.…”

Section: Preliminariesmentioning

confidence: 99%

“…Yager [10] further acquainted the notion of q-ROFS as an enlargement of PFS. A short time ago, Pythagorean m-polar fuzzy sets with their practical implementations have been unveiled by Naeem et al [11,12]. Well along, Riaz et al [13] extended the notion of soft sets towards Pythagorean m-polar fuzzy soft sets and prooffered some fascinating utilizations of this model.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Pythagorean $m$ -Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

Riaz

Naeem

Chinram

et al. 2021

Journal of Mathematics

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The role of multipolar uncertain statistics cannot be unheeded while confronting daily life problems on well-founded basis. Fusion (aggregation) of a number of input values in multipolar form into a sole multipolar output value is an essential tool not merely of physics or mathematics but also of widely held problems of economics, commerce and trade, engineering, social sciences, decision-making problems, life sciences, and many more. The problem of aggregation is very wide-ranging and fascinating, in general. We use, in this article, Pythagorean fuzzy numbers (PFNs) in multipolar form to contrive imprecise information. We introduce Pythagorean m -polar fuzzy weighted averaging (P m FWA), Pythagorean m -polar fuzzy weighted geometric (P m FWG), symmetric Pythagorean m -polar fuzzy weighted averaging (SP m FWA), and symmetric Pythagorean m -polar fuzzy weighted geometric (SP m FWG) operators for aggregating uncertain data. Finally, we present a practical example to illustrate the application of the proposed operators and to demonstrate its practicality and effectiveness towards investment strategic decision making.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Pythagorean $m$ -Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

Riaz

Naeem

Chinram

et al. 2021

Journal of Mathematics

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“…Many researchers presented the idea of an intuitionistic cubic fuzzy set (ICFS) [26] and its applications in decision-making. Naeem et al [27] developed the new idea of Pythagorean m-polar fuzzy sets with the TOPSIS method and their applications in the selection of advertisement mode. After that, Ayaz et al [28] introduced the generalized idea of SCFS, which is the generalization of ICFS and PCFS, and discussed its application in the selection of enterprise performance.…”

Section: Introductionmentioning

confidence: 99%

Spherical Cubic Fuzzy Extended TOPSIS Method and Its Application in Multicriteria Decision-Making

Tehreem

Hussain

Alsanad

et al. 2021

Mathematical Problems in Engineering

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This paper aims to propose a new methodology for spherical cubic fuzzy (SCF) multicriteria decision-making (MCDM) utilizing the TOPSIS method that uses incomplete weight information. At first, the maximum deviation model is suggested to determine the criteria of weight values. An MCDM methodology is introduced using SCF information, based on the proposed method. Also, to validate the effectiveness of the proposed information, a numerical example is given. Finally, a comprehensive and structured analysis of existing work in comparison with previous work is given.

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“…[ 13 ], Hashmi and Riaz [ 14 ], Karaaslan [ 15 ], Karaaslan and Hunu [ 16 ], Naeem et al . [ 17 ], Peng and Yang [ 18 ], Peng et al . [ 19 ], Peng and Selvachandran [ 20 ], Peng and Liu [ 21 ], Riaz and Hashmi [ 22 ] and Riaz et al .…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets

Riaz

Hamid

Afzal³

et al. 2021

PLoS ONE

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q-Rung orthopair fuzzy set (qROFS) and m-polar fuzzy set (mPFS) are rudimentary concepts in the computational intelligence, which have diverse applications in fuzzy modeling and decision making under uncertainty. The aim of this paper is to introduce the hybrid concept of q-rung orthopair m-polar fuzzy set (qROmPFS) as a hybrid model of q-rung orthopair fuzzy set and m-polar fuzzy set. A qROmPFS has the ability to deal with real life situations when decision experts are interested to deal with multi-polarity as well as membership and non-membership grades to the alternatives in an extended domain with q-ROF environment. Certain operations on qROmPFSs and several new notions like support, core, height, concentration, dilation, α-cut and (α, β)-cut of qROmPFS are defined. Additionally, grey relational analysis (GRA) and choice value method (CVM) are presented under qROmPFSs for multi-criteria decision making (MCDM) in robotic agri-farming. The proposed methods are suitable to find out an appropriate mode of farming among several kinds of agri-farming. The applications of proposed MCDM approaches are illustrated by respective numerical examples. To justify the feasibility, superiority and reliability of proposed techniques, the comparison analysis of the final ranking in the robotic agri-farming computed by the proposed techniques with some existing MCDM methods is also given.

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Pythagorean m-polar fuzzy sets and TOPSIS method for the selection of advertisement mode

Cited by 57 publications

References 50 publications

Pythagorean $m$ -Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

Pythagorean $m$ -Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

Spherical Cubic Fuzzy Extended TOPSIS Method and Its Application in Multicriteria Decision-Making

Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets

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Pythagorean m-polar fuzzy sets and TOPSIS method for the selection of advertisement mode

Cited by 57 publications

References 50 publications

Pythagoreanm-Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

Pythagoreanm-Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

Spherical Cubic Fuzzy Extended TOPSIS Method and Its Application in Multicriteria Decision-Making

Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets

Contact Info

Product

Resources

About

Pythagorean $m$ -Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

Pythagorean $m$ -Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making