Renovating Neutrosophic Systems Through a New Generalization Methodology

Neutrosophic sets are effective in handling uncertainty but face computational challenges with large datasets. This paper introduces a generalized strategy to enhance neutrosophic systems. Compared to traditional methods, the proposed approach improves classification accuracy by 4.4% and precision by 5.1%, while being 20% slower for datasets over 10,000 entries. Confusion matrix analysis confirms higher reliability with fewer false positives and negatives. Graphical results highlight its applicability in complex decision-making. The study demonstrates improved efficiency and robustness, with future research focusing on AI integration for broader real-world applications.