A binary logarithm similarity measure with roughness approximation of rough neutrosophic set for covid-19 / Suriana Alias ...[et al.]
Roughness measures for uncertainty data occur with less consideration since the data involve indeterminacy and inconsistency. The indeterminacy plus inconsistency can be solved by a rough neutrosophic set with roughness approximation. Therefore, a binary logarithm similarity measure for a rough neut...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Book |
Published: |
Universiti Teknologi MARA, Kelantan,
2023-01-20.
|
Subjects: | |
Online Access: | Link Metadata |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
MARC
LEADER | 00000 am a22000003u 4500 | ||
---|---|---|---|
001 | repouitm_89056 | ||
042 | |a dc | ||
100 | 1 | 0 | |a Alias, Suriana |e author |
700 | 1 | 0 | |a Mustapha, Norzieha |e author |
700 | 1 | 0 | |a Md Yasin, Roliza |e author |
700 | 1 | 0 | |a Abd Rhani, Norarida |e author |
700 | 1 | 0 | |a Yaso, Muhammad Naim Haikal |e author |
700 | 1 | 0 | |a Ramlee, Hazlin Shahira |e author |
245 | 0 | 0 | |a A binary logarithm similarity measure with roughness approximation of rough neutrosophic set for covid-19 / Suriana Alias ...[et al.] |
260 | |b Universiti Teknologi MARA, Kelantan, |c 2023-01-20. | ||
500 | |a https://ir.uitm.edu.my/id/eprint/89056/1/89056.pdf | ||
520 | |a Roughness measures for uncertainty data occur with less consideration since the data involve indeterminacy and inconsistency. The indeterminacy plus inconsistency can be solved by a rough neutrosophic set with roughness approximation. Therefore, a binary logarithm similarity measure for a rough neutrosophic set with roughness approximation was proposed in this research. A rough neutrosophic set was chosen as the uncertainty set theory information, which includes the upper and lower approximation with a boundary set approximation. The objectives of this research are to define a binary logarithm similarity measure for a rough neutrosophic set, to formulate the properties satisfied by the proposed similarity measure, and to develop a decision-making model by using a bina1y logarithm similarity measure for a case study (COVID-19). The roughness approximation was used in the derivation of the binary logarithm similarity measure. The proving result was finalized. Then, the derivation of binary logarithm similarity measures of a rough neutrosophic set was well defined. As a validation process, the similarity properties for identifying the most important priority group for COVID-19 vaccines were used such as age, health state, women, and job types. Following that, the decision-making for identifying the most important priority group for COVID-19 vaccines is presented. | ||
546 | |a en | ||
690 | |a Analysis | ||
690 | |a Algorithms | ||
655 | 7 | |a Article |2 local | |
655 | 7 | |a PeerReviewed |2 local | |
787 | 0 | |n https://ir.uitm.edu.my/id/eprint/89056/ | |
787 | 0 | |n https://journal.uitm.edu.my/ojs/index.php/JMCS | |
856 | 4 | 1 | |u https://ir.uitm.edu.my/id/eprint/89056/ |z Link Metadata |