A New Entropy Measure for Fermatean Fuzzy Set Using the COPRAS Method for Operating System Selection
20th INDIACom; 13th International Conference on Computing for Sustainable Global Development, New Delhi, Hindistan, 8 - 10 Nisan 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.23919/indiacom70271.2026.11526338
- Basıldığı Şehir: New Delhi
- Basıldığı Ülke: Hindistan
- Anahtar Kelimeler: COPRAS Method, Entropy Measures, Fermatean Fuzzy Set(FFS), Intuitionistic Fuzzy Sets (IFS), Multi-Criteria Decision Making, Operating system selection, Pythagorean Fuzzy Sets (PFS)
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Choosing the right operating system is a complex process that involves multiple factors, including security, performance, 'compatibility and support' and cost. System security and performance in particular generally conflict with each other. An operating system is typically selected based on multiple criteria. Therefore, the decision-making process is complicated and expert evaluations often involve uncertainty and imprecision. Due to this uncertainty in assessing different criteria, many researchers have adopted Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS) to manage imprecise information. However IFS and PFS do not provide sufficient flexibility in defining membership and non-membership degrees. In this study, Fermatean Fuzzy Sets (FFSs) are employed because they offer greater flexibility through a cubic constraint condition compared to traditional fuzzy set models. Entropy-based methods are utilized to objectively determine the weights of criteria, thereby reducing bias in expert judgments. An entropy measure for FFSs is introduced in this work and the ranking of operating system alternatives under the FFS environment is carried out using the COPRAS method. A comparative analysis of the proposed FFS entropy measure with existing entropy measures demonstrates that the proposed measure has a lower standard deviation and greater stability. These findings indicates that the proposed methodology effectively captures uncertainty and provides reliable rankings for operating system selection problems.