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Neural Computing and Applications

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01.12.2015 | Original Article

Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment

verfasst von: Surapati Pramanik, Pranab Biswas, Bibhas C. Giri

Erschienen in: Neural Computing and Applications | Ausgabe 5/2017

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Abstract

In this paper, we propose new vector similarity measures of single-valued and interval neutrosophic sets by hybridizing the concepts of Dice and cosine similarity measures. We present their applications in multi-attribute decision making under neutrosophic environment. We use these similarity measures to find out the best alternative by determining the similarity measure values between the ideal alternative and each alternative. The results of the proposed similarity measures have been validated by comparing with other existing similarity measures reported in the literature for multi-attribute decision making. The main thrust of the proposed similarity measures will be in the field of practical decision making, medical diagnosis, pattern recognition, data mining, clustering analysis, etc.

Vorheriger Artikel Evolving neural networks and fuzzy clustering for multireservoir operations

Nächster Artikel Cross-entropy measure on interval neutrosophic sets and its applications in multicriteria decision making

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Titel: Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment
verfasst von: Surapati Pramanik
Pranab Biswas
Bibhas C. Giri
Publikationsdatum: 01.12.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-2125-3

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