Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure

To deal with situations involving uncertainty, Fermatean fuzzy sets are more effective than Pythagorean sets, intuitionistic and sets. Applications for similarity measures can be found in a wide range of fields, including clustering analysis, classification issues, medical diagnosis, etc. The computation the weights criteria multi-criteria decision-making problem heavily relies on entropy measurements. In this paper, we employ t-conorms to suggest various measures. We have also discussed all their interesting characteristics. Using suggested measurements, created some new By using numerical comparison linguistic hedging, established superiority metrics over existing environment. usefulness proposed measurements is shown by pattern analysis. Last but not least, novel multi-attribute approach described that tackles significant flaw order preference ideal solution, conventional decision-making,