Evaluations for medical diagnoses phenomena through $$2\times 2$$ linguistic neutrosophic environment-based game situation

Bhaumik, Ankan; Roy, Sankar Kumar

doi:10.1007/s00500-022-06881-8

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Example 2.2. Let A = ( [3,7], [2,12]) and B = ( [4,9], [2,10]) be two rough intervals. Then, A(A < B) = 6.25− (6) 3.25+3.5 = 0.25 6.75 = 1 27 = 0.038 ∈ (0, 1).…”

Section: Nested Rough Intervalsmentioning

confidence: 99%

“…Savku and Weber discussed on stochastic games for investment problems, finance problems [46,47]. Several articles have been devoted extensively on fuzzy matrix games (Bhaumik et al [5][6][7], Bhaumik and Roy [2,3], Jana and Roy [24,26,27], Osman et al [37], Roy and Mondal [43], Li [28]); and the matrix game with uncertain payoffs has been studied by several authors in different directions in the last few years (Bhurjee and Panda [8], Chandra and Aggarwal [10], Cheng et al [11], Gao et al [13,14], Li [23,29,30,32], Singh et al [48]).…”

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confidence: 99%

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Solving matrix game using rough interval payoffs

Ghosh,

Bhaumik,

Weber

et al. 2024

JDG

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In this paper, we conduct an analysis of a matrix game using rough interval payoffs, which we refer to as the rough interval matrix game (RIMG). We employ three methods to address the problem at hand: linear programming, graphical method, and algebraic method. The proposed approach offers several key advantages over existing methods, which we highlight. We introduce algorithms associated with each of the three mentioned methods to obtain optimal solutions for the RIMG. To validate the effectiveness of our proposed techniques, we present two numerical examples and apply the methodologies to solve them. In conclusion, we summarize the findings of our study and discuss potential avenues for future research based on the insights provided in this paper.

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“…Example 2.2. Let A = ( [3,7], [2,12]) and B = ( [4,9], [2,10]) be two rough intervals. Then, A(A < B) = 6.25− (6) 3.25+3.5 = 0.25 6.75 = 1 27 = 0.038 ∈ (0, 1).…”

Section: Nested Rough Intervalsmentioning

confidence: 99%

mentioning

confidence: 99%

Solving matrix game using rough interval payoffs

Ghosh,

Bhaumik,

Weber

et al. 2024

JDG

Self Cite

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“…Various similarity and entropy measures have been introduced. They have successfully applied in solving problems in real-life applications, such as medical diagnosis [4][5][6], pattern recognition [7,8], decision making [9][10][11], and clustering analysis [12,13].…”

Section: Introductionmentioning

confidence: 99%

New Similarity and Entropy Measures of Temporal Complex Neutrosophic Set and Its application in Multi-Criteria Decision Making

Lan,

Thong,

Giang

et al. 2023

Preprint

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The Temporal complex neutrosophic set (TCNS) is an effective structure for dealing with information that has uncertain, indeterminate, and time-related factors in decision-making problems. In the last decade, many researchers focused on measuring the uncertainty and vagueness in knowledge using the properties of neutrosophic sets or extensions of neutrosophic sets. Information measures such as similarity and entropy measures are valuable tools for solving real-world multi-criteria decision-making (MCDM) problems. However, the existing method did not care or interest in the information measures of TCNS. This paper proposes several new similarity and entropy measures under the TCNS environment. The proposed measures have been verified and proven to comply with the axiomatic definition of the similarity and entropy measure for the TCNS. Finally, a numerical example related to choosing a tourist destination in Vietnam is given to demonstrate the practical applicability of the proposed measures. The numerical example proves that the proposed similarity and entropy measures of TCNS can produce accurate and reasonable results for decision-making problems in the real world.

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