Akademik Veri Yönetim Sistemi
The 2nd International Conference on Big Data Computing and Modeling (ICBDCM2025), Beijing, Çin, 27 - 28 Mart 2025, ss.1-12, (Tam Metin Bildiri)
One important and widely used method for dealing with
uncertainty in various fields is the Fermatean Fuzzy Set theory. In contrast to
intuitionistic and Pythagorean fuzzy sets, Fermatean fuzzy sets promise to
handle vagueness better by offering a cubic sum of membership and
non-membership values. There are Fermatean fuzzy information systems for conflicts
where Fermatean fuzzy numbers represent agents' attitudes toward topics. To
demonstrate how to compute "positive," "neutral," and
"negative" alliances in Fermatean fuzzy information systems for
conflicts, we first introduce the concepts of "positive," "neutral,"
and "negative" alliances with two thresholds. We then use examples to
support our arguments. Next, using examples to demonstrate how to calculate the
"positive," "neutral," and "negative" alignments
using an expert's Fermatean fuzzy loss function, we concentrate on three-way
conflict analysis based on the Bayesian minimal risk theory. The second goal of
this work is to propose two novel multi-measure-based techniques under
uncertainty represented by Fermatean fuzzy sets. We present three conflict measures
about a subject using the maximum "positive" and "negative"
agents. The final goal of this research is to propose two novel
multi-measure-based techniques under uncertainty represented by Fermatean fuzzy
sets. Using maximal "positive" and "negative" agents
(support degrees, opposition degrees, and both support and opposition degrees),
we present three different conflict metrics for a subject. We combine two
fundamental tasks, sectioning, and fusion, to provide two models for multiple
challenges.