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  • Azim, Ahmad Bin  (3)
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  • 1
    Online Resource
    Online Resource
    Industry 4.0 project prioritization by using q-spherical fuzzy rough analytic hierarchy process
    American Institute of Mathematical Sciences (AIMS) ; 2023
    In:  AIMS Mathematics Vol. 8, No. 8 ( 2023), p. 18809-18832
    Details
    In: AIMS Mathematics, American Institute of Mathematical Sciences (AIMS), Vol. 8, No. 8 ( 2023), p. 18809-18832
    Abstract: 〈abstract〉 〈p〉The Fourth Industrial Revolution, also known as Industry 4.0, is attracting a significant amount of attention because it has the potential to revolutionize a variety of industries by developing a production system that is fully automated and digitally integrated. The implementation of this transformation, however, calls for a significant investment of resources and may present difficulties in the process of adapting existing technology to new endeavors. Researchers have proposed integrating the Analytic Hierarchy Process (AHP) with extensions of fuzzy rough sets, such as the three-dimensional q-spherical fuzzy rough set (q-SFRS), which is effective in handling uncertainty and quantifying expert judgments, to prioritize projects related to Industry 4.0. This would allow the projects to be ranked in order of importance. In this article, a novel framework is presented that combines AHP with q-SFRS. To calculate aggregated values, the new framework uses a new formula called the q-spherical fuzzy rough arithmetic mean, when applied to a problem involving the selection of a project with five criteria for evaluation and four possible alternatives, the suggested framework produces results that are robust and competitive in comparison to those produced by other multi-criteria decision-making approaches.〈/p〉 〈/abstract〉
    Type of Medium: Online Resource
    ISSN: 2473-6988
    URL: Article
    DOI: 10.3934/math.2023957
    Language: Unknown
    Publisher: American Institute of Mathematical Sciences (AIMS)
    Publication Date: 2023
    detail.hit.zdb_id: 2917342-5
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  • 2
    Online Resource
    Online Resource
    q-Spherical fuzzy rough sets and their usage in multi-attribute decision-making problems
    American Institute of Mathematical Sciences (AIMS) ; 2023
    In:  AIMS Mathematics Vol. 8, No. 4 ( 2023), p. 8210-8248
    Details
    In: AIMS Mathematics, American Institute of Mathematical Sciences (AIMS), Vol. 8, No. 4 ( 2023), p. 8210-8248
    Abstract: 〈abstract〉 〈p〉This article's purpose is to investigate and generalize the concepts of rough set, in addition to the q-spherical fuzzy set, and to introduce a novel concept that is called q-spherical fuzzy rough set (q-SFRS). This novel approach avoids the complications of more recent ideas like the intuitionistic fuzzy rough set, Pythagorean fuzzy rough set, and q-rung orthopair fuzzy rough set. Since mathematical operations known as "aggregation operators" are used to bring together sets of data. Popular aggregation operations include the arithmetic mean and the weighted mean. The key distinction between the weighted mean and the arithmetic mean is that the latter allows us to weight the various values based on their importance. Various aggregation operators make different assumptions about the input (data kinds) and the kind of information that may be included in the model. Because of this, some new q-spherical fuzzy rough weighted arithmetic mean operator and q-spherical fuzzy rough weighted geometric mean operator have been introduced. The developed operators are more general. Because the picture fuzzy rough weighted arithmetic mean (PFRWAM) operator, picture fuzzy rough weighted geometric mean (PFRWGM) operator, spherical fuzzy rough weighted arithmetic mean (SFRWAM) operator and spherical fuzzy rough weighted geometric mean (SFRWGM) operator are all the special cases of the q-SFRWAM and q-SFRWGM operators. When parameter q = 1, the q-SFRWAM operator reduces the PFRWAM operator, and the q-SFRWGM operator reduces the PFRWGM operator. When parameter q = 2, the q-SFRWAM operator reduces the SFRWAM operator, and the q-SFRWGM operator reduces the SFRWGM operator. Besides, our approach is more flexible, and decision-makers can choose different values of parameter q according to the different risk attitudes. In addition, the basic properties of these newly presented operators have been analyzed in great depth and expounded upon. Additionally, a technique called multi-criteria decision-making (MCDM) has been established, and a detailed example has been supplied to back up the recently introduced work. An evaluation of the offered methodology is established at the article's conclusion. The results of this research show that, compared to the q-spherical fuzzy set, our method is better and more effective.〈/p〉 〈/abstract〉
    Type of Medium: Online Resource
    ISSN: 2473-6988
    URL: Article
    DOI: 10.3934/math.2023415
    Language: Unknown
    Publisher: American Institute of Mathematical Sciences (AIMS)
    Publication Date: 2023
    detail.hit.zdb_id: 2917342-5
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  • 3
    Online Resource
    Online Resource
    Averaging aggregation operators under the environment of 〈i〉q〈/i〉-rung orthopair picture fuzzy soft sets and their applications in MADM problems
    American Institute of Mathematical Sciences (AIMS) ; 2023
    In:  AIMS Mathematics Vol. 8, No. 4 ( 2023), p. 9027-9053
    Details
    In: AIMS Mathematics, American Institute of Mathematical Sciences (AIMS), Vol. 8, No. 4 ( 2023), p. 9027-9053
    Abstract: 〈abstract〉〈p〉〈italic〉q〈/italic〉-Rung orthopair fuzzy soft set handles the uncertainties and vagueness by membership and non-membership degree with attributes, here is no information about the neutral degree so to cover this gap and get a generalized structure, we present hybrid of picture fuzzy set and 〈italic〉q〈/italic〉-rung orthopair fuzzy soft set and initiate the notion of 〈italic〉q〈/italic〉-rung orthopair picture fuzzy soft set, which is characterized by positive, neutral and negative membership degree with attributes. The main contribution of this article is to investigate the basic operations and some averaging aggregation operators like 〈italic〉q〈/italic〉-rung orthopair picture fuzzy soft weighted averaging operator and 〈italic〉q〈/italic〉-rung orthopair picture fuzzy soft order weighted averaging operator under the environment of 〈italic〉q〈/italic〉-rung orthopair picture fuzzy soft set. Moreover, some fundamental properties and results of these aggregation operators are studied, and based on these proposed operators we presented a stepwise algorithm for MADM by taking the problem related to medical diagnosis under the environment of 〈italic〉q〈/italic〉-rung orthopair picture fuzzy soft set and finally, for the superiority we presented comparison analysis of proposed operators with existing operators.〈/p〉〈/abstract〉
    Type of Medium: Online Resource
    ISSN: 2473-6988
    URL: Article
    DOI: 10.3934/math.2023452
    Language: Unknown
    Publisher: American Institute of Mathematical Sciences (AIMS)
    Publication Date: 2023
    detail.hit.zdb_id: 2917342-5
    Location Call Number Limitation Availability
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    Online Resource
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