4.6 Article

Interval Neutrosophic Reducible Weighted Maclaurin Symmetric Means With Internet of Medical Things (IoMt) Industry Evaluation

Journal

IEEE ACCESS
Volume 7, Issue -, Pages 62479-62495

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2916791

Keywords

IoMT; interval neutrosophic set; aggregation operator; idempotency; reducible weighted MSM

Funding

  1. National Natural Science Foundation of China [61462019]
  2. Ministry of Education (MOE), China
  3. Project of Humanities and Social Sciences [18YJCZH054]
  4. Natural Science Foundation of Guangdong Province [2018A030307033, 2018A0303130274]
  5. Social Science Foundation of Guangdong Province [GD18CFX06]

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The Internet of Medical Things (IoMT) is a global infrastructure composing of plentiful applications and medical devices that are interconnected by ICT. In considering the problem of the IoMT industry evaluation, the requisite issue that concerns strong interaction and incertitude. The Maclaurin symmetric mean (MSM), as a resultful information concordant instrument, can capture the interrelation among multiple arguments more efficiently. The abundance of the weighted MSMs has been presented to manage the different uncertain information aggregation issues by reason that the attribute variables are frequently diverse. However, these existing weighted form of MSM operators fail to possess the fundamental properties of idempotency and reducibility. To solve the above issues, we explore the interval neutrosophic reducible weighted MSM (INRWMSM) operator and the interval neutrosophic reducible weighted dual MSM (INRWDMSM) operator. Moreover, momentous properties and some special cases of the INRWMSM and INRWDMSM operators are discussed in detail. Whereafter, we propose some multiple attribute decision making (MADM) algorithms based on INRWMSM and INRWDMSM. The availability of proposed algorithms is stated by an IoMT evaluation issue. Finally, a comparison of the developed with the existing interval neutrosophic decision making algorithms has been formed for showing their validity.

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