Abstract
Linguistic single-valued neutrosophic (LSVN) set (LSVNS) is one of the influential contrivance for addressing the decision-making (DM) problems with uncertain and qualitative information by means of degree of acceptance, indeterminacy and non-acceptance in linguistic terms. In DM problems, similarity measure is the basic tool for recognizing associations within or across the given choices. Thus, this paper aims to construct the parametric similarity measure and weighted parametric similarity measure by making use of LSVNSs. The basic axioms of these measures are also highlighted. Further, the manuscript offers the multi-criteria DM approach based on the proposed measures and describes it by a numerical example. Finally, the efficiency and its preferences over the existing methods are confirmed by means of sensitivity and comparative investigation.
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Acknowledgments
The author would like to thank the University Grant Commission, New Delhi, India for providing financial support under Maulana Azad National Fellowship scheme wide File No. F1-17.1/2017-18/MANF-2017-18-PUN-82613/(SA-III/Website) during the preparation of this manuscript.
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Nancy (2020). Parametric Similarity Measures on Linguistic Single-Valued Neutrosophic Sets with Application to Decision-Making Problems. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_94
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DOI: https://doi.org/10.1007/978-3-030-16657-1_94
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