2018
DOI: 10.2174/1874110x01812010136
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Fuzzy Decision Making in Medical Diagnosis Using an Advanced Distance Measure on Intuitionistic Fuzzy Sets

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Cited by 30 publications
(9 citation statements)
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“…𝑅 1 (0.4, 0.6, 1) (0.5, 0.5, 0.9) (0.6, 0.4, 0.8) (0.7, 0.3, 0.7) (0.8, 0.2, 0.6) (0.9, 0.1, 0.5) (1, 0, 0.4) 𝑅 2 (0.8, 0.4, 0.1) (0.5, 0.6, 0.2) (1, 0.7, 0.8) (0.3, 0.2, 0.9) (0.6, 0.7, 0.8) (0.9, 1, 0) (0, 0, 1) 𝑅 3 (0.2, 0.1, 0) (0.3, 0.6, 0.7) (0.3, 0.2, 0.1) (0.7, 0.8, 1) (0, 0, 1) (0.6, 0.7, 0.3) (0.3, 0.2, 0.1) 𝑅 4 (0.6, 0.5, 0.2) (0.7, 0, 0) (0.5, 0.5, 0.5) (0.6, 0.5, 0.1) (0.8, 0.8, 0.8) (0.3, 0.7, 0.6) (0.7, 0.8, 0.1) 𝑅 5 (0.3, 0.3, 0.3) (0, 0, 1) (0.6, 0.5, 0.3) (0.4, 0.5, 0.6) (0.3, 0.2, 0.1) (0.2, 0.1, 0.1) (0.5, 0.6, 0.1) 𝐶 1 (0.4, 0.5, 1) (0.4, 0.5, 0.6) (0.8, 0.6, 0.1) (0.9, 0, 0) (0.1, 0.1, 0.1) 𝐶 2 (0.8, 0.1, 0.1) 0.6, 0.5, 0.5) (0.3, 0.2, 0.1) (0.8, 0.9, 1) (0, 0, 1) 𝐶 3 (1, 0, 0) (0.6, 0.4, 0.2) (0.5, 0.8, 0.9) (1, 0.3, 0.5) (0.3, 0.4, 0) 𝐶 4 (0.5, 0.6, 1) (0, 0, 0.3) (0, 0.3, 0.3) (0.6, 0.3, 0.1) (0.7, 0.8, 0.1) 𝐶 5 (0.6, 0.3, 0.1) (0.5, 0.3, 0.1) (0.7, 0.9, 0.3) (0.1, 0.6, 0) (0.5, 0.2, 0.1) 𝐶 6 (0, 0.7, 1) (0.5, 0.4, 0.2) (0.3, 0.2, 1) (0.5, 0.4, 0.1) (0.9, 0, 1) 𝐶 7 (0.8, 0.1, 0.3) (0.7, 0.5, 0.6) (0.8, 0.9, 0.1) (0.6, 0.7, 0.2) (0, 0, 1)…”
Section: Table: 51 Students Vs Subjectsmentioning
confidence: 99%
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“…𝑅 1 (0.4, 0.6, 1) (0.5, 0.5, 0.9) (0.6, 0.4, 0.8) (0.7, 0.3, 0.7) (0.8, 0.2, 0.6) (0.9, 0.1, 0.5) (1, 0, 0.4) 𝑅 2 (0.8, 0.4, 0.1) (0.5, 0.6, 0.2) (1, 0.7, 0.8) (0.3, 0.2, 0.9) (0.6, 0.7, 0.8) (0.9, 1, 0) (0, 0, 1) 𝑅 3 (0.2, 0.1, 0) (0.3, 0.6, 0.7) (0.3, 0.2, 0.1) (0.7, 0.8, 1) (0, 0, 1) (0.6, 0.7, 0.3) (0.3, 0.2, 0.1) 𝑅 4 (0.6, 0.5, 0.2) (0.7, 0, 0) (0.5, 0.5, 0.5) (0.6, 0.5, 0.1) (0.8, 0.8, 0.8) (0.3, 0.7, 0.6) (0.7, 0.8, 0.1) 𝑅 5 (0.3, 0.3, 0.3) (0, 0, 1) (0.6, 0.5, 0.3) (0.4, 0.5, 0.6) (0.3, 0.2, 0.1) (0.2, 0.1, 0.1) (0.5, 0.6, 0.1) 𝐶 1 (0.4, 0.5, 1) (0.4, 0.5, 0.6) (0.8, 0.6, 0.1) (0.9, 0, 0) (0.1, 0.1, 0.1) 𝐶 2 (0.8, 0.1, 0.1) 0.6, 0.5, 0.5) (0.3, 0.2, 0.1) (0.8, 0.9, 1) (0, 0, 1) 𝐶 3 (1, 0, 0) (0.6, 0.4, 0.2) (0.5, 0.8, 0.9) (1, 0.3, 0.5) (0.3, 0.4, 0) 𝐶 4 (0.5, 0.6, 1) (0, 0, 0.3) (0, 0.3, 0.3) (0.6, 0.3, 0.1) (0.7, 0.8, 0.1) 𝐶 5 (0.6, 0.3, 0.1) (0.5, 0.3, 0.1) (0.7, 0.9, 0.3) (0.1, 0.6, 0) (0.5, 0.2, 0.1) 𝐶 6 (0, 0.7, 1) (0.5, 0.4, 0.2) (0.3, 0.2, 1) (0.5, 0.4, 0.1) (0.9, 0, 1) 𝐶 7 (0.8, 0.1, 0.3) (0.7, 0.5, 0.6) (0.8, 0.9, 0.1) (0.6, 0.7, 0.2) (0, 0, 1)…”
Section: Table: 51 Students Vs Subjectsmentioning
confidence: 99%
“…
In this paper, we introduce a distance measure on single-valued neutrosophic sets which is a generalization of intuitionistic fuzzy distance measure (Dutta and Goala 2018). This distance measure satisfies the axioms of metric (George F. Simmons 1963) on single-valued neutrosophic sets and shows that the difference of distance measure from unity is a similarity measure.
…”
mentioning
confidence: 99%
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“…Luo and Zhao [19] gave the algorithms for pattern recognition and use it to solve medical diagnosis problems. Gupta and Tiwari [20] and Datta and Goala [21] proposed cosine similarity measure for intuitionistic and interval-valued intuitionistic fuzzy sets using an advanced distance measure on intuitionistic fuzzy sets.…”
Section: Introductionmentioning
confidence: 99%
“…Eyoh, I. et al also introduced an intuitionistic fuzzy logic for regression problems that was of interval Type-2 fuzzy sets [30]. An advanced distance measurement technique on intuitionistic fuzzy set-in decision making was also studied [31]. Samuel, A.E.…”
Section: Introductionmentioning
confidence: 99%