A Novel Approach Toward Roughness of Bipolar Soft Sets and Their Applications in MCGDM

Gul, Rizwan; Shabir, Muhammad; Naz, Munazza; Aslam, Muhammad

doi:10.1109/access.2021.3116097

Cited by 7 publications

(7 citation statements)

References 61 publications

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“…In hybrid fuzzy and bipolar fuzzy techniques, the membership function depends on the choice and thinking of the DMS which makes the results more biased. (iii) When we apply the current MCGDM techniques presented in [19], [25], and [51] to our Example 12, we obtain the following ranking among the diseases (shown in TABLE 7), and the corresponding graphical portrayal is displayed in Figure 3. From TABLE 7, we observe that the optimal solution via all four methods is the same, making our technique is feasible and effective.…”

Section: B Comparison With Other Approachesmentioning

confidence: 99%

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Multigranulation Modified Rough Bipolar Soft Sets and Their Applications in Decision-Making

et al. 2022

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The classical theory of rough sets (RSs) established by Pawlak, mainly focused on the approximation of sets characterized by a single equivalence relation (ER) over the universe. However, most of the current single granulation structure models cannot meet the user demand or the target of solving problems. Multi-granulation rough sets (MGRS) can better deal with the problems where data might be spread over various locations. In this article, based on modified rough bipolar soft sets (MRBSs), the concept of multi-granulation MRBSs (MGMRBSs) is introduced. A finite collection of bipolar soft sets (BSs) has been used for this purpose. Several important structural properties and results of the suggested model are carefully analyzed. Meanwhile, to measure the uncertainty of MGMRBSs, some important measures associated with MGMRBSs are presented in MGMRBS-approximation space, and some of their interesting properties are examined. In the framework of multi-granulation, we developed optimistic MGMRBSs (OMGMRBSs) and pessimistic MGMRBSs (PMGMRBSs). The relationships among the MGMRBSs, OMGMRBSs, and PMGMRBSs are also established. After that, a novel multi-criteria group decisionmaking (MCGDM) approach based on OMGMRBSs is developed to solve some problems in decisionmaking (DM). The basic principles and the detailed steps of the DM model are presented in detail. To demonstrate the applicability and potentiality of the developed model, we give a practical example of a medical diagnosis. Finally, we conduct a comparative study of the proposed MCGDM approach with some existing techniques to endorse the advantages of the proposed model.INDEX TERMS MGRS, MRBSs, MGMRBS-approximations, MCGDM.

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Section: B Comparison With Other Approachesmentioning

confidence: 99%

“…Akram and Ali [1] suggested a technique for DM based on rough Pythagorean fuzzy bipolar soft information. Gul et al [19] developed a novel approach towards the roughness of BSs and applied this approach in MCGDM. Malik and Shabir [36] use rough bipolar fuzzy approximations based consensus model.…”

Section: Introductionmentioning

confidence: 99%

Multigranulation Modified Rough Bipolar Soft Sets and Their Applications in Decision-Making

et al. 2022

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“…eorem (see [31]). Let ∅ ≠ S, ∅ ≠ R be the subset of K d -module M and (Γ, C) and (c, C) be SRRE on M. en, the following hold for all u ∈ C: 15. eorem (see [31]).…”

Section: Eorem (Seementioning

confidence: 99%

“…In [15], roughness of bipolar soft sets and their related applications are discussed. In [16], Feng et al presented the relationship between soft and rough sets and proposed rough soft sets and soft rough sets.…”

Section: Introductionmentioning

confidence: 99%

Soft Relations Applied to the Substructures of Quantale Module and Their Approximation

et al. 2022

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This research article offers a study on a new relation of rough sets and soft sets with an algebraic structure quantale module by using soft reflexive and soft compatible relations. The lower approximation and upper approximation of subsets of quantale module are utilized by aftersets and foresets. As a sequel of this relation, different characterizations of rough soft substructures of quantale modules are obtained. To ensure the results, soft reflective and soft compatible relations are focused and these are interpreted by aftersets and foresets. Furthermore, the algebraic relations between upper (lower) approximation of substructures of quantale module and the upper (lower) approximations of their homomorphic images with the help of quantale module homomorphism are examined. In comparison with the different type of approximations in different type of algebraic structures, it is concluded that this new study is much better.

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“…Shabir and Gul [30] established and discussed the modified rough bipolar soft sets (MRBSs) in MCGDM. Gul et al [31] presented a new technique for determining the roughness of BSSs and examined their applicability to MCGDM. Mahmood et al [32] suggested a complex fuzzy N-SS and DM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Novel Bipolar Soft Rough-Set Approximations and Their Application in Solving Decision-Making Problems

Gul

Shabir

Mashwani

et al. 2022

IJFIS

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The rough set (RS) theory is a successful approach for studying the uncertainty in data. In contrast, the bipolar soft sets (BSS) can deal with the uncertainty, as well as bipolarity of the data in many situations. In 2018, Karaaslan and C ¸agman proposed bipolar soft rough sets (BSRSs), a hybridization of RS and BSS. However, certain shortcomings with BSRS violate Pawlak's RS theory. To overcome these shortcomings, the concept of the modified bipolar soft rough set (MBSRS) has been proposed in this study. Moreover, this idea has been investigated through illustrative examples, where the important properties are inspected deeply. Furthermore, certain significant measures associated with MBSRS are also provided. Finally, an application of the MBSRS to multi-attribute group decision-making (MAGDM) problems is proposed. In addition, among various alternatives, an algorithm for decisionmaking accompanied by a practical example is presented as the optimal alternative . A brief comparative analysis of the proposed approach with some existing techniques is also provided to indicate the validity, flexibility, and superiority of the suggested MAGDM model.

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A Novel Approach Toward Roughness of Bipolar Soft Sets and Their Applications in MCGDM

Cited by 7 publications

References 61 publications

Multigranulation Modified Rough Bipolar Soft Sets and Their Applications in Decision-Making

Multigranulation Modified Rough Bipolar Soft Sets and Their Applications in Decision-Making

Soft Relations Applied to the Substructures of Quantale Module and Their Approximation

Novel Bipolar Soft Rough-Set Approximations and Their Application in Solving Decision-Making Problems

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