Research productivity assessment using complex fermatean fuzzy soft set-based entropy measures

What is it about?

It is about the characterization of a novel theoretical framework called complex fermatean fuzzy soft set to tackle the issues related to modelling uncertainties, vagueness, and periodicity of information. It is a combination of three significant concepts: fermatean fuzzy set, soft set, and complex fuzzy soft set, and generalizes them. The propoed idea has been employed to assess the research productivity.

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Photo by Patrick Tomasso on Unsplash

Photo by Patrick Tomasso on Unsplash

Why is it important?

This article is worth reading as it effectively addresses several critical issues in decision-making and uncertainty modeling. Firstly, it tackles the uncertain nature of parameters, ensuring that variations and imprecisions in data are systematically handled. Secondly, it considers the biased attitude of decision-makers, integrating mechanisms to minimize subjective influence and enhance rational decision-making. Thirdly, it accounts for the periodicity of information, recognizing that data and conditions may change over time and incorporating this dynamism into the analysis. Lastly, it addresses information-based uncertainties and vagueness, providing robust methodologies to manage incomplete, imprecise, or inconsistent information. These aspects make the study highly relevant for researchers and practitioners dealing with complex decision-making scenarios.

Perspectives

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Writing this article was both a rewarding and enriching experience, as it was co-authored with researchers with whom I have shared a long-standing and strong collaboration. The process allowed us to combine our expertise, insights, and mutual understanding, fostering a productive and seamless exchange of ideas. Working with trusted colleagues not only enhanced the quality of the research but also made the journey more enjoyable and fulfilling. This collaboration reinforced our professional bonds and underscored the value of teamwork in advancing knowledge in our field.

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