A novel Schweizer Sklar aggregation framework for fuzzy bipolar soft numbers in multi-criteria decision-making: application to deep learning model evaluation

Key Points

• The aggregation framework improves evaluation processes for deep learning models, providing a structured method for decision-making.
• Key findings indicate enhanced decision-making accuracy based on fuzzy bipolar soft numbers, optimizing model comparisons.
• Analysis utilizes a novel aggregation framework within multi-criteria decision-making contexts, demonstrating its applicability.
• Implications support more effective evaluations in diverse contexts, highlighting potential advancements in decision-making methodologies.