Smart Computing and Decision Support with Neutrosophic Ranked Soft Sets

Neutrosophic Ranked Soft Sets (NRSS) provide a robust mathematical framework for managing uncertainty, imprecision, and parameter prioritization in complex decision environments. This paper presents a comprehensive study on the application of NRSS in intelligent computing and data-driven decision-making systems. By integrating neutrosophic logic with ranked soft set theory, the proposed model effectively captures the degrees of truth, uncertainty, and falsity along with their relative weight of parameters. We establish the theoretical foundations of NRSS and illustrate its utility through real-world-inspired scenarios in areas such as expert systems, intelligent filtering, and multi-criteria evaluation. The findings demonstrate that NRSS signifi cantly enhance the adaptability, interpretability, and reliability of intelligent systems, making them well-suited for modern decision support applications.