Rough Neutrosophic Digraphs with Application

Sayed, Sidra; Ishfaq, Nabeela; Akram, Muhammad; Smarandache, Florentín

doi:10.3390/axioms7010005

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…So, the graph has been constructed by applying the concept of the hybrid model. It builds self‐complementary digraph for rough neutrosophic sets in decision making cases [15].…”

Section: Neutrosophic Setmentioning

confidence: 99%

Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Sangeetha

Amalanathan

2020

CAAI trans. intell. technol.

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Neutrosophy is the study of neutralities, which is an extension of discussing the truth of opinions. Neutrosophic logic can be applied to any field, to provide the solution for indeterminacy problem. Many of the real-world data have a problem of inconsistency, indeterminacy and incompleteness. Fuzzy sets provide a solution for uncertainties, and intuitionistic fuzzy sets handle incomplete information, but both concepts failed to handle indeterminate information. To handle this complicated situation, researchers require a powerful mathematical tool, naming, neutrosophic sets, which is a generalised concept of fuzzy and intuitionistic fuzzy sets. Neutrosophic sets provide a solution for both incomplete and indeterminate information. It has mainly three degrees of membership such as truth, indeterminacy and falsity. Boolean values are obtained from the three degrees of membership by cut relation method. Data items which contrast from other objects by their qualities are outliers. The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute. From the obtained weighted values, the threshold value is fixed to determine outliers. Experimental analysis of the proposed method has been carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to prove its performance.

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“…So, the graph has been constructed by applying the concept of the hybrid model. It builds self‐complementary digraph for rough neutrosophic sets in decision making cases [15].…”

Section: Neutrosophic Setmentioning

confidence: 99%

Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Sangeetha

Amalanathan

2020

CAAI trans. intell. technol.

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“…Obviously, a bipolar neutrosophic graph structure can deal with more than one factors involved for particular type of relationship between any two vertices, which is properly explained in this research paper with some interesting real-life applications. For other notations, terminologies and applications are not mentioned in this research article; the readers are referred to Alcantud (2016), Ali et al (2016), Dinesh (2011), Greco and Kadzinski (2018), Luo et al (2018), Majumdar and Samanta (2014), Mordeson and Nair (2001), Myithili et al (2016), Pramanik et al (2016), Sayed et al (2018), Smarandache (1999), Turksen (1986), Wu et al (2017) and Zhan et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Novel decision-making method based on bipolar neutrosophic information

2018

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Neutrosophic fusion of rough set theory: An overview

Zhang

Kang

et al. 2020

Computers in Industry

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Rough Neutrosophic Digraphs with Application

Cited by 8 publications

References 19 publications

Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Novel decision-making method based on bipolar neutrosophic information

Neutrosophic fusion of rough set theory: An overview

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