Theoretical framework for a decision support system for micro-enterprise supermarket investment risk assessment using novel picture fuzzy hypersoft graph

Saeed, Muhammad; Harl, Muhammad Imran; Mekawy, Ibrahim

doi:10.1371/journal.pone.0273642

Cited by 9 publications

(9 citation statements)

References 51 publications

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“…Jeevitha et.al [24] discussed the problem of climate crisis by utilizing LDF DEMA-TEL approach. Subsequently, some enhancements of LDFS were addressed and discussed the application in the field of digital transformation [23], agri-drone [21], tender selection [22] medical diagnosis [45] Saeed et al used hybrid fuzzy hypersoft structures for optimization and efficient decisionmaking purposes [42]- [44].…”

Section: A Literature Reviewmentioning

confidence: 99%

Linear Diophantine Fuzzy Clustering Algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

Kannan,

Jayakumar,

Saeed

et al. 2024

IEEE Access

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The significance of clustering algorithms lies in their ability to distinguish problems and devise customized solutions. In the broader context of clustering, fuzzy clustering is one of the crucial aspects. In response to the real-world clustering problems, this research suggests a new fuzzy cluster scheme of data under the linear diophantine fuzzy set(LDFS) framework. More precisely, LDF clustering is initiated with the aid of the correlation coefficient(CC) and weighted correlation coefficient(WCC) for LDFS. Due to their ability to quantify the degree of similarity between two elements, CC are valuable in clustering problems. The LDF-clustering algorithm comprises a well-integrated algorithm for managing uncertainty and CC among LDFS. Also, our approach to LDF clustering is compared to existing fuzzy clustering studies to assess its effectiveness. Since LDFS broadens the score space, the experimental evaluation of our proposed scheme enables Decision makers(DM) to freely select their score values. The theme of this study is to impart the commencement of LDF-clustering analysis and attempt to apply CC to the clustering problem.An interpretative example provides the analysis of the logistic efficiency of food products by employing an LDF-clustering algorithm.

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Section: A Literature Reviewmentioning

confidence: 99%

Linear Diophantine Fuzzy Clustering Algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

Kannan,

Jayakumar,

Saeed

et al. 2024

IEEE Access

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“…The fundamentals, characteristics, and operations of HS sets have been extensively investigated [ 33 ]. Extensions of HS sets, including rough HS sets [ 34 ], plithogenic HS sets [ 35 ], and interval-valued fuzzy HS sets [ 36 ], picture fuzzy HS sets [ 37 ], have been explored, alongside discussions on their practical applications. Asaad and Musa have utilized HS sets to examine various topological concepts [ 38 , 39 ].…”

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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“…Saeed [17] introduced the notion of picture fuzzy hypersoft sets (PF HSSs ), which is used to handle attributes and subattributes, making them particularly valuable when dealing with inconsistent information. Building on the concept of PF HSSs , Saeed [18] introduced the idea of picture fuzzy hypersoft graphs (PFHSG).…”

Section: Introductionmentioning

confidence: 99%

Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets

Imran Harl,

Saeed,

Saeed

et al. 2024

IEEE Access

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The selection of an optimal investment sector is of critical importance not only for individual financial success but also to drive economic development. The allocation of capital into sectors with high potential for growth, innovation, and job creation is key. In addressing the complexity of decisionmaking scenarios associated with investment sector exploration, we introduce a novel data structure known as Picture fuzzy hypersoft set (PF HSSs ). This specialized approach within computational intelligence and decision-making aims to categorize data into various attributes and sub-attributes, considering the significant role of neutrality. The study stems from the need for a comprehensive framework (PFHSSs) that can effectively handle intricate decision-making scenarios involving attributes, subattributes, and nuanced factors such as neutrality. Traditional tools such as TOPSIS and its extensions of fuzzy sets, while robust in Multiple Criteria Decision Making (MCDM), may face challenges in modeling and analyzing decisionmaking information within a PFHSSs environment. The rationale behind this study lies in enhancing the accuracy and efficiency of decision-making processes when dealing with complex, fuzzy, and multicriteria data. By introducing newly proposed distances and similarity measures tailored to PFHSSs, and constructing a PFHSSs-TOPSIS method, we aim to address the limitations faced by existing models in the PF HSSs environment. The application of Hamming distance-based similarity measures further distinguishes our method by determining the weights assigned to each decision maker. The proposed PF HSSs -TOPSIS method is practically applied in designing an optimal investment sector exploration tool for investors. This method has the potential to establish a crucial connection between alternatives and attributes, providing value across various fields and industries. The research emphasizes bridging the gap in decisionmaking scenarios where alternatives and attributes need to be effectively connected and analyzed, thereby contributing to the advancement of decision-making processes in complex domains.

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Theoretical framework for a decision support system for micro-enterprise supermarket investment risk assessment using novel picture fuzzy hypersoft graph

Cited by 9 publications

References 51 publications

Linear Diophantine Fuzzy Clustering Algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

Linear Diophantine Fuzzy Clustering Algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

N-bipolar hypersoft sets: Enhancing decision-making algorithms

Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets

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