Hesitant Fuzzy Graphs and Their Products

Javaid, Muhammad; Kashif, Agha; Rashid, Tabasam

doi:10.1080/16168658.2020.1817658

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Pathinathan et al 38 initiated the idea of hesitant fuzzy graph. Javaid et al 39 proposed numerous operations on hesitant fuzzy graphs. Moreover, Akram and Saira 40 introduced the notion of fuzzy soft graphs and they also presented the applications of fuzzy soft graphs in social and road networks 41 .…”

Section: Introductionmentioning

confidence: 99%

On t-intuitionistic fuzzy graphs: a comprehensive analysis and application in poverty reduction

Razzaque,

Masmali,

Latif

et al. 2023

Sci Rep

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This paper explains the idea of t-intuitionistic fuzzy graphs as a powerful way to analyze and display relationships that are difficult to understand. The article also illustrates the ability of t-intuitionistic fuzzy graphs to establish complex relationships with multiple factors or dimensions of a physical situation under consideration. Moreover, the fundamental set operations of t-intuitionistic fuzzy graphs are proposed. The notions of homomorphism and isomorphism of t-intuitionistic fuzzy graphs are also introduced. Furthermore, the paper highlights a practical application of the proposed technique in the context of poverty reduction within a specific society. By employing t-intuitionistic fuzzy graphs, the research demonstrates the potential to address the multifaceted nature of poverty, considering various contributing factors and their interdependencies. This application showcases the versatility and effectiveness of t-intuitionistic fuzzy graphs as a tool for decision-making and policy planning in complex societal issues.

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Section: Introductionmentioning

confidence: 99%

On t-intuitionistic fuzzy graphs: a comprehensive analysis and application in poverty reduction

Razzaque,

Masmali,

Latif

et al. 2023

Sci Rep

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show abstract

“…The concept of a fuzzy graph was initially introduced by Kauffman [10], but the development of the fuzzy graphs theory is due to Rosenfeld [11] and Yeh and Bang [12]. The fuzzy graphs theory has been developing in different directions, such as fuzzy tolerance graphs [13], fuzzy threshold graphs [14], interval-valued fuzzy graphs [15][16][17], fuzzy k-competition graphs and p-competition fuzzy graphs [18], m−step fuzzy competition graphs [19], and hesitant fuzzy graphs [20]. In 1994, Zhang [21,22] introduced the concept of bipolar fuzzy sets as a generalization of fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers

Ivanova

Durcheva

2023

Mathematics

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The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper proposes a method for facilitating the design of analog amplifiers based on m-polar fuzzy graphs theory and deep learning. M-polar fuzzy graphs are used because of their flexibility and the possibility to model different real-life multi-attribute problems. Deep learning is applied to solve a regression task and to predict the membership functions of the m-polar fuzzy graph vertices (the solutions), taking on the role of domain experts. The performance of the learner is high since the obtained errors are very small: Root Mean Squared Error is from 0.0032 to 0.0187, Absolute Error is from 0.022 to 0.098 and Relative Error is between 0.27% and 1.57%. The proposed method is verified through the design of three amplifiers: summing amplifier, subtracting amplifier, and summing/subtracting amplifier. The method can be used for improving the design process of electronic circuits with the possibility of automating some tasks.

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“…HFGs was introduced by Pathinathan et al [24] and extended in [25,26]. Javaid et al [27] studied new results of HFGs and their products. Karaaslan [28] investigated the HFGs and their applications in decision-making.…”

Section: Introductionmentioning

confidence: 99%

A study on vague-valued hesitant fuzzy graph with application

et al. 2022

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The hesitant fuzzy graph (HFG) is one of the most powerful tools to find the strongest influential person in a network. Many problems of practical interest can be modeled and solved by using HFG algorithms. HFGs, belonging to the FG family, have good capabilities when faced with problems that cannot be expressed by FGs. The vague-valued hesitant fuzzy graph (VVHFG) is the generalization of the HFG. A VVHFG is a powerful and useful tool to find the influential person in various parts, such as meetings, conferences, and every group discussion. In this study, we introduce a new concept of the VVHFG. Our purpose is to develop a notion of the VVHFG and also to present some basic definitions, notations, remarks, and proofs related to VVHFGs. We propose a numerical method to find the most dominating person using our proposed work. Finally, an application of the VVHFG in decision-making has been introduced.

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Hesitant Fuzzy Graphs and Their Products

Cited by 10 publications

References 21 publications

On t-intuitionistic fuzzy graphs: a comprehensive analysis and application in poverty reduction

On t-intuitionistic fuzzy graphs: a comprehensive analysis and application in poverty reduction

M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers

A study on vague-valued hesitant fuzzy graph with application

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