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Logarithmic cubic aggregation operators and their application in online study effect during Covid-19
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<abstract><p>The aims of this study is to define a cubic fuzzy set based logarithmic decision-making strategy for dealing with uncertainty. Firstly, we illustrate some logarithmic operations for cubic numbers (CNs). The cubic set implements a more pragmatic technique to communicate the uncertainties in the data to cope with decision-making difficulties as the observation of the set. In fuzzy decision making situations, cubic aggregation operators are extremely important. Many aggregation operations based on the algebraic t-norm and t-conorm have been developed to cope with aggregate uncertainty expressed in the form of cubic sets. Logarithmic operational guidelines are factors that help to aggregate unclear and inaccurate data. We define a series of logarithmic averaging and geometric aggregation operators. Finally, applying cubic fuzzy information, a creative algorithm technique for analyzing multi-attribute group decision making (MAGDM) problems was proposed. We compare the suggested aggregation operators to existing methods to prove their superiority and validity, and we find that our proposed method is more effective and reliable as a result of the comparison and sensitivity analysis.</p></abstract>
American Institute of Mathematical Sciences (AIMS)
Title: Logarithmic cubic aggregation operators and their application in online study effect during Covid-19
Description:
<abstract><p>The aims of this study is to define a cubic fuzzy set based logarithmic decision-making strategy for dealing with uncertainty.
Firstly, we illustrate some logarithmic operations for cubic numbers (CNs).
The cubic set implements a more pragmatic technique to communicate the uncertainties in the data to cope with decision-making difficulties as the observation of the set.
In fuzzy decision making situations, cubic aggregation operators are extremely important.
Many aggregation operations based on the algebraic t-norm and t-conorm have been developed to cope with aggregate uncertainty expressed in the form of cubic sets.
Logarithmic operational guidelines are factors that help to aggregate unclear and inaccurate data.
We define a series of logarithmic averaging and geometric aggregation operators.
Finally, applying cubic fuzzy information, a creative algorithm technique for analyzing multi-attribute group decision making (MAGDM) problems was proposed.
We compare the suggested aggregation operators to existing methods to prove their superiority and validity, and we find that our proposed method is more effective and reliable as a result of the comparison and sensitivity analysis.
</p></abstract>.
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