Decision-Making by Using TOPSIS Techniques in the Framework of Bipolar Complex Intuitionistic Fuzzy N-Soft Sets

Mahmood, Tahir; Rehman, Ubaid Ur; Shahab, Sana; Ali, Zeeshan; Anjum, Mohd

doi:10.1109/access.2023.3316879

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Fatimah et al [ 17 ] introduced N -soft sets as a broader concept than soft sets. Several novel hybrid models such as N -bipolar soft sets [ 18 ], N -polar soft sets [ 19 ], complex fermatean fuzzy N -soft sets [ 20 ], bipolar fuzzy N -soft sets [ 21 ], bipolar complex fuzzy N-soft set [ 22 ], bipolar complex intuitionistic fuzzy N-soft [ 23 ], probabilistic hesitant N -soft sets [ 24 ], fuzzy N -soft expert sets [ 25 ], spherical fuzzy N-soft expert sets [ 26 ], Pythagorean fuzzy N-soft expert sets [ 27 ], have been developed by researchers. These models demonstrate the effective handling of hybrid situations by N -soft sets.…”

Section: Introductionmentioning

confidence: 99%

N-bipolar hypersoft sets: Enhancing decision-making algorithms

Musa

2024

PLoS ONE

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This paper introduces N-bipolar hypersoft (N-BHS) sets, a versatile extension of bipolar hypersoft (BHS) sets designed to effectively manage evaluations encompassing both binary and non-binary data, thereby exhibiting heightened versatility. The major contributions of this framework are twofold: Firstly, the N-BHS set introduces a parameterized representation of the universe, providing a nuanced and finite granularity in perceiving attributes, thereby distinguishing itself from conventional binary BHS sets and continuous fuzzy BHS sets. Secondly, this model signifies a new area of research aimed at overcoming limitations inherent in the N-bipolar soft set when handling multi-argument approximate functions. Through the strategic partitioning of attributes into distinct subattribute values using disjoint sets, the N-BHS set emerges as a powerful tool for effectively addressing uncertainty-related problems. In pursuit of these objectives, the paper outlines various algebraic definitions, including incomplete N-BHS sets, efficient N-BHS sets, normalized N-BHS sets, equivalence under normalization, N-BHS complements, and BHS sets derived from a threshold, exemplified through illustrative examples. Additionally, the article explores set-theoretic operations within the N-BHS sets framework, such as relative null/whole N-BHS sets, N-BHS subsets, and two distinct approaches to N-BHS extended/restricted union and intersection. Finally, it proposes and compares decision-making methodologies regarding N-BHS sets, including a comprehensive comparison with relevant existing models.

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Section: Introductionmentioning

confidence: 99%

N-bipolar hypersoft sets: Enhancing decision-making algorithms

Musa

2024

PLoS ONE

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Selection of Cloud Services Provider by Utilizing Multi-Attribute Decision-Making Based on Hesitant Bipolar Complex Fuzzy Dombi Aggregation Operators

Aslam,

Waqas,

Rehman

et al. 2024

IEEE Access

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A trustworthy cloud services provider makes sure that data backups, disaster recovery, and compliance with industry standards are all met, allowing businesses to concentrate on their core competencies and innovation while leaving the complexity of IT infrastructure management to the professionals. The selection of the finest cloud services provider is a multi-attribute decision-making problem and thus, in this article, we investigate the selection of the finest cloud services provider by employing a multi-attribute decision-making technique using the invention of hesitant bipolar complex fuzzy set. The hesitant bipolar complex fuzzy sets deal not only with the data involving complex and hesitant information but also the data of dual aspects that are both positive and negative because a lot of real-life situation contains information that has hesitancy, 2 nd dimension, and a dual aspect simultaneously. Furthermore, in this manuscript, using the structure of a hesitant bipolar complex fuzzy set we initiate numerous aggregation operators. Under the environment of the proposed methodology, we invent different aggregation operators such as hesitant bipolar complex fuzzy Dombi weighted averaging, hesitant bipolar complex fuzzy Dombi ordered weighted averaging, hesitant bipolar complex fuzzy Dombi hybrid averaging, hesitant bipolar complex fuzzy Dombi weighted geometric, hesitant bipolar complex fuzzy Dombi ordered weighted geometric and hesitant bipolar complex fuzzy Dombi hybrid geometric operators. All these operators are used to solve different situations at different times. When any decision-maker wants to solve hesitation under a complex environment with 2 nd dimensions then these operators are very useful. Without proposed operators, we cannot handle the above situations because no doubt different tools for different situations are available in the market but there is no tool available for solving hesitant bipolar complex fuzzy information. Afterward, we devise a technique of multi-attribute decision-making for tackling genuine life decision-making dilemmas under the setting of hesitant bipolar complex fuzzy sets. We investigate a case study associated with cloud service providers by employing the invented technique of decision-making. In the end, we compare our newly suggested work with a few other current theories to show the effectiveness of our work. INDEX TERMSCloud services; cloud services provider; hesitant bipolar complex fuzzy set; MADM.

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Gamified approach towards optimizing supplier selection through Pythagorean Fuzzy soft-max aggregation operators for healthcare applications

Shahab,

Anjum,

Dutta

et al. 2024

MATH

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<abstract><p>The soft-max function, a well-known extension of the logistic function, has been extensively utilized in numerous stochastic classification methodologies, such as linear differential analysis, soft-max extrapolation, naive Bayes detectors, and neural networks. The focus of this study is the development of soft-max based fuzzy aggregation operators (AOs) for Pythagorean fuzzy sets (PyFS), capitalizing on the benefits provided by the soft-max function. In addition to introducing these novel AOs, we also present a comprehensive approach to multi-attribute decision-making (MADM) that employs the proposed operators. To demonstrate the efficacy and applicability of our MADM method, we applied it to a real-world problem involving Pythagorean fuzzy data. The analysis of supplier selection has been extensively examined in many academic works as a crucial component of supply chain management (SCM), recognised as a significant MADM challenge. The process of choosing healthcare suppliers is a pivotal element that has the potential to greatly influence the efficacy and calibre of healthcare provisions. In addition, we given a numerical example to rigorously evaluate the accuracy and dependability of the proposed procedures. This examination demonstrates the effectiveness and potential of our proposed soft-max based AOs and their applicability in Pythagorean fuzzy environments.</p></abstract>

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Decision-Making by Using TOPSIS Techniques in the Framework of Bipolar Complex Intuitionistic Fuzzy N-Soft Sets

Cited by 3 publications

References 49 publications

N-bipolar hypersoft sets: Enhancing decision-making algorithms

N-bipolar hypersoft sets: Enhancing decision-making algorithms

Selection of Cloud Services Provider by Utilizing Multi-Attribute Decision-Making Based on Hesitant Bipolar Complex Fuzzy Dombi Aggregation Operators

Gamified approach towards optimizing supplier selection through Pythagorean Fuzzy soft-max aggregation operators for healthcare applications

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