Soft Rough q-Rung Orthopair m-Polar Fuzzy Sets and q-Rung Orthopair m-Polar Fuzzy Soft Rough Sets and Their Applications

Ping, Jingshui; Atef, Mohammed; Khalil, Ahmed Mostafa; Riaz, Muhammad; Hassan, Nasruddin

doi:10.1109/access.2021.3118055

Cited by 8 publications

(2 citation statements)

References 66 publications

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“…As it is well-known that the interior and closure topological operators behave similarly to the lower and upper approximations; so, in forthcoming works, we plan to study the counterparts of these models via topological structures. In addition, we will benefit from the hybridization of rough set theory with some approaches such as soft sets and fuzzy sets [30], [34] to introduce these approximation spaces via these hybridized frames and show their role in efficiently dealing with uncertain knowledge.…”

Section: Rank1mentioning

confidence: 99%

Improvement of Approximation Spaces Using Maximal Left Neighborhoods and Ideals

Al-shami

Hosny

2022

IEEE Access

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Rough set theory was introduced by Pawlak, in 1982, as a methodology to discover structural relationships within imprecise and uncertain data. This theory has been generalized using the idea of neighborhood systems to be more efficient to get rid of uncertainty and deal with a wide scope of practical applications. Motivated by this idea, in this work, we initiate novel generalized rough set models using the concepts of "maximal left neighborhoods and ideals". Their basic features are studied and the relationships between them are revealed. The main merits of these models, as we prove, are first to preserve almost all major properties of approximation operators with respect to the Pawlak model. Second, they keep the monotonic property, which leads to an efficient evaluation of the uncertainty in the data, and third, these models enlarge the knowledge gotten from the information systems because they minimize the vagueness regions more than some previous models. We complete this manuscript by applying the proposed approach to analyze educational data and illustrate its role to improve the obtained classifications of objects and show the great performance of the present approach against other ones. Elucidative examples that support the obtained results are provided.

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Section: Rank1mentioning

confidence: 99%

Improvement of Approximation Spaces Using Maximal Left Neighborhoods and Ideals

Al-shami

Hosny

2022

IEEE Access

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“…This path of study also included some topology's extensions such as minimal structure [15,17] and bitopology [36]. Hybridization of rough sets with some uncertainty tools such as soft and fuzzy sets was investigated in [32,34].…”

Section: Introductionmentioning

confidence: 99%

Topological approach to generate new rough set models

Al-shami

2022

Complex Intell. Syst.

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In this paper, we introduce a topological method to produce new rough set models. This method is based on the idea of “somewhat open sets” which is one of the celebrated generalizations of open sets. We first generate some topologies from the different types of $$N_\rho $$ N ρ -neighborhoods. Then, we define new types of rough approximations and accuracy measures with respect to somewhat open and somewhat closed sets. We study their main properties and prove that the accuracy and roughness measures preserve the monotonic property. One of the unique properties of these approximations is the possibility of comparing between them. We also compare our approach with the previous ones, and show that it is more accurate than those induced from open, $$\alpha $$ α -open, and semi-open sets. Moreover, we examine the effectiveness of the followed method in a problem of Dengue fever. Finally, we discuss the strengths and limitations of our approach and propose some future work.

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A novel methodology for multi-agent decision-making based on N-soft sets

2023

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Multi-criteria decision analysis with multiple agents provides tools for the research and development of intelligent applications. The solution strategy depends largely on the structure of the data. In this paper we propose a decide-then-merge approach to the problem of multi-agent multi-criteria decision-making when each individual data comes in the form of an N-soft set. To this purpose, we present a methodology that works in two steps. First, with each individual input we associate a ranking. This can be done in several ways, since individual decisions based on N-soft sets are well developed. Then with the help of voting theory, we merge all these rankings and produce a final ranking of the alternatives. The steps of this method are explained with synthetic examples. We argue that the proposed strategy is highly adaptable because both stages of our scheme can be customized by the user. A real case study in the framework of Operational Research illustrates the application of this method in detail. Finally, we perform a comparative analysis with existing methodologies.

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Soft Rough q-Rung Orthopair m-Polar Fuzzy Sets and q-Rung Orthopair m-Polar Fuzzy Soft Rough Sets and Their Applications

Cited by 8 publications

References 66 publications

Improvement of Approximation Spaces Using Maximal Left Neighborhoods and Ideals

Improvement of Approximation Spaces Using Maximal Left Neighborhoods and Ideals

Topological approach to generate new rough set models

A novel methodology for multi-agent decision-making based on N-soft sets

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