Linear Diophantine Fuzzy Rough Sets: A New Rough Set Approach with Decision Making

Ayub, Saba; Shabir, Muhammad; Riaz, Muhammad; Mahmood, Waqas; Božanić, Darko; Marinković, Dragan

doi:10.3390/sym14030525

Cited by 32 publications

(23 citation statements)

References 68 publications

(97 reference statements)

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“…The invented operator's efficiency is proved by comparing it with various existing operators and explaining the advantages and geometrical representation of the diagnosed approaches to describe the feasibility and worth of the presented work. In the upcoming scenario, we utilise the theory of aggregation operators with the help of confidence level in the environment of linear Diophantine FSs [43], spherical linear Diophantine FSs [44], linear Diophantine rough sets [45], similarity measures [46], fuzzy N‐soft sets [47], hesitant fuzzy N‐soft sets [48], Pythagorean fuzzy N‐soft sets [30], complex spherical FSs [49], complex T‐spherical fuzzy sets [50], T‐spherical fuzzy sets [51], complex T‐spherical fuzzy relation [52], hesitant fuzzy linguistic [53], and decision‐making [54, 55]. Based on these existing theories we will try to utilise the CRITIC‐VIKOR method to enhance the worth of the new theory.…”

Section: Discussionmentioning

confidence: 99%

Aggregation operators and CRITIC‐VIKOR method for confidence complex q‐rung orthopair normal fuzzy information and their applications

Mahmood

Ali

Naeem

2022

CAAI Trans on Intel Tech

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Supply chain management is an essential part of an organisation's sustainable programme. Understanding the concentration of natural environment, public, and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential. To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role. Keeping in mind this role, the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy (CQRONF) information in supply chain management. The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging, confidence CQRONF ordered weighted averaging, confidence CQRONF hybrid averaging, confidence CQRONF weighted geometric, confidence CQRONF ordered weighted geometric, confidence CQRONF hybrid geometric operators and try to diagnose various properties and results. Furthermore, with the help of the CRITIC and VIKOR models, we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method. Moreover, in the availability of diagnosed operators, we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas. Finally, the initiated operator's efficiency is proved by comparative analysis. K E Y W O R D Saveraging/geometric aggregation operators, complex q-rung orthopair normal fuzzy information, confidence levels, strategic decision-making methodsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

Aggregation operators and CRITIC‐VIKOR method for confidence complex q‐rung orthopair normal fuzzy information and their applications

Mahmood

Ali

Naeem

2022

CAAI Trans on Intel Tech

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“…An comparative analysis has been undertaken under the IFSs [11] by taking the IvIFS as zero and Pythagorean fuzzy sets [37] and existing approach of Al-shami et al [50], during analysis the weight vector is 𝑒 = (0.2,0.3,0.5) 𝑇 to justify the superiority of our suggested mean operator over existing alternatives. The best possible score results and the alternative's ranking order are summarized in Table 4.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Many researchers produced its application in MADM [32,33,34,35]. After that, the concept of RS theory was proposed by Pawlak [36] and FS and RS were applied to different fields [37,38,39,40].…”

Section: Introductionmentioning

confidence: 99%

Multiple Attribute Decision-Making Based on Bonferroni Mean Operators under Square Root Fuzzy Set Environment

Radenović

Ali

Shaheen

et al. 2022

JCCE

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The intuitionistic fuzzy set, which has a membership and non-membership degree, is a controlling and effective device for dealing with fuzziness and uncertainty. Recently, the square root fuzzy set (SR-FS) which is one of the efficient generalizations of an intuitionistic fuzzy set (IFS) for dealing with uncertainty and haziness in information has been introduced. In this study, a novel method for multiple attribute decision-making (MADM) based on SR-Fuzzy information is investigated. Since aggregation operators are significant in the decision-making process. To achieve this goal, the current paper suggests a variety of novel Bonferroni Mean and Weighted Bonferroni Mean operators to aggregate the SR-Fuzzy values for the various decision-maker preferences. SR-Fuzzy Bonferroni Mean (SRFBM) operator and weighted SR-Fuzzy Bonferroni Mean (WSRFBM) operator are established and describes their properties. Then, we construct a MADM approach using the proposed operators for the SR-Fuzzy information and proved the approach with a mathematical example. In the end, a comparative study of the developed and existing approaches has been discussed to evaluate the pertinency and practicality of the proposed DM technique.

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“…A lot of researchers utilize the conception of RSs in many areas [18][19]. Afterwards, Ayub et al [20] initiated the conception of linear Diophantine fuzzy RSs and provide its application to DM issues. Many researchers had developed the combined concept of RSs and FSs theory, such as the idea of the fuzzy rough sets (FRSs) been initiated by Dubois and Prade [21].…”

Section: Introductionmentioning

confidence: 99%

Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis

et al. 2023

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In recent days, due to the complexities of different diseases of similar types, it becomes very difficult to diagnose an accurate type of a disease, and so medical diagnosis becomes a difficult task for the experts working in health departments. Many researchers try to develop new methods and techniques to over the difficulties that come across in the way of medical diagnosis. In this paper, we try to develop some novel techniques that will help the experts to diagnose the diseases accurately. Based on a more advanced structure of intuitionistic fuzzy rough sets, in this article, we establish confidence level intuitionistic fuzzy average/geometric aggregation operators to incorporate the familiarity degree of experts with evaluated objects for an initial assessment while intuitionistic fuzzy rough average/geometric aggregation operators cannot do so. Moreover, we have given some basic properties of the initiated operators. To show the effective use of these operators we have proposed an algorithm with an illustrative example. Furthermore, based on the intuitionistic fuzzy rough model, we have also established a medical diagnosis model to incorporate the difficulty that occurs in the diagnosis of disease. Furthermore, a comparative analysis demonstrates its efficiency of our proposed methods.

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Linear Diophantine Fuzzy Rough Sets: A New Rough Set Approach with Decision Making

Cited by 32 publications

References 68 publications

Aggregation operators and CRITIC‐VIKOR method for confidence complex q‐rung orthopair normal fuzzy information and their applications

Aggregation operators and CRITIC‐VIKOR method for confidence complex q‐rung orthopair normal fuzzy information and their applications

Multiple Attribute Decision-Making Based on Bonferroni Mean Operators under Square Root Fuzzy Set Environment

Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis

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