Applications on TFN-valued shannon entropy and TGC-integrals

Key Points

• Applications of TFN-valued Shannon entropy enhance probabilistic analysis in information theory.
• Key advancements include the introduction of new TGC-integrals to handle complex data measures.
• Analysis focuses on integrating information theory frameworks to model uncertainty in data.
• Implications highlight the potential for improved algorithms in statistical and computational methods.