TY  - EJOU
AU  - Sarwar, Muhammad Amad 
AU  - Gong, Yuezheng 
AU  - Alzakari, Sarah A. 
AU  - Alhussan, Amel Ali 

TI  - Quantum-Driven Spherical Fuzzy Model for Best Gate Security Systems
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 3
SN  - 1526-1506

AB  - Global security threats have motivated organizations to adopt robust and reliable security systems to ensure the safety of individuals and assets. Biometric authentication systems offer a strong solution. However, choosing the best security system requires a structured decision-making framework, especially in complex scenarios involving multiple criteria. To address this problem, we develop a novel quantum spherical fuzzy technique for order preference by similarity to ideal solution (QSF-TOPSIS) methodology, integrating quantum mechanics principles and fuzzy theory. The proposed approach enhances decision-making accuracy, handles uncertainty, and incorporates criteria relationships. Criteria weights are determined using spherical fuzzy sets, and alternatives are ranked through the QSF-TOPSIS framework. This comprehensive multi-criteria decision-making (MCDM) approach is applied to identify the optimal gate security system for an organization, considering critical factors such as accuracy, cost, and reliability. Additionally, the study compares the proposed approach with other established MCDM methods. The results confirm the alignment of rankings across these methods, demonstrating the robustness and reliability of the QSF-TOPSIS framework. The study identifies the infrared recognition and identification system (IRIS) as the most effective, with a score value of 0.5280 and optimal security system among the evaluated alternatives. This research contributes to the growing literature on quantum-enhanced decision-making models and offers a practical framework for solving complex, real-world problems involving uncertainty and ambiguity.
KW  - Quantum spherical fuzzy numbers; security systems; decision-making; criteria weights; TOPSIS method

DO  - 10.32604/cmes.2025.066356