فهرست نشریات علمی دسترسی آزاد ایران

In the evolving field of decision science, accurately measuring similarity under uncertainty remains a critical challenge. Despite significant advancements in developing CSMs for IFS and IVIFS, these approaches fall short of capturing the dual characteristics inherent in CIFS environments. By integrating the features of IFS and IVIFS, CIFS offers a unified mathematical framework that effectively addresses both uncertainty and vagueness in decision-making. To address this gap, we propose three novel CSMs for CIFS based on the angle cosine of two vectors, distance, and a cosine function-based measure. Mathematical properties such as boundedness, symmetry, and identity are proven for each proposed measure, ensuring their theoretical soundness. Furthermore, we extend these measures into weighted forms to improve the flexibility. The practical applicability of the measures is demonstrated through real-world case studies in pattern recognition and medical diagnosis. A numerical example, complemented by comparative analysis, validates the proposed model by demonstrating how our approach outperforms existing similarity measures.