Citation

Deivanayagam Pillai, Nagarajan and Thangavel, Bhuvaneswari and Suppiah, Yasothei and Anbazhagan, Kanchana A Fusion-Based Framework for Neutrosophic Entropy and Attribute Weighting Using Linguistic D-Numbers. Big Data and Computing Visions, 5 (4). pp. 342-368. ISSN 27834956

Text article_229588_eddd8e70a346adcb0c8ed2cb2b560e96.pdf - Published Version Restricted to Repository staff only Download (790kB)

Abstract

Decision-making with uncertainty and indeterminacy are the standard problems that occur in the real world, in which precise evaluations are difficult to be made most of the time. Uncertainty and indeterminacy cause decision-making that is not always reliable to be difficult, particularly when linguistic assessments and incomplete information are involved. Current methods are not able to represent such features at the same time. In order to solve the issue, we have come up with a new idea that merges linguistic categories with D numbers to create linguistic D numbers, whereby the depiction of fuzzy evaluation data is not only feasible but accurate as well. A novel Neutrosophic entropy measure for Neutrosophic D-Numbers (NDNs) is made available which, as opposed to the standard entropy, retains the confidence degrees and the Linguistic Terms (LT) at the same time. Furthermore, a different combination rule is introduced with the aim of strengthening the union of the uncertain and indeterminate pieces of information. Besides these innovations, we have also made changes to the Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) method for it to be applicable in the NDNs scenario of Multiple Attribute Decision-Making (MADM). Our experimental results not only show the superiority of our method in determining attribute weights over other models but also the stability of the rankings. These improvements illustrate the novel integration of entropy, fusion, and decision-making in a single NDNs framework.

Item Type:	Article
Uncontrolled Keywords:	Neutrosophic entropy
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Feb 2026 07:15
Last Modified:	06 Feb 2026 07:15
URII:	http://shdl.mmu.edu.my/id/eprint/15203

Downloads

Edit (login required)