AI-Based Decision Making Using Neutrosophic Graphs

Abstract

Artificial Intelligence (AI) has become an essential technology for solving complex decision-making problems in healthcare, finance, transportation, cybersecurity, and industrial automation. Conventional graph-based models often fail to represent uncertain and inconsistent information present in real-world applications. Neutrosophic graph theory provides a flexible mathematical framework by incorporating truth, indeterminacy, and falsity memberships into graph structures. This paper presents an AI-based decision-making framework using neutrosophic graphs to model uncertain relationships among alternatives. The proposed methodology combines graph representation with intelligent ranking techniques to improve decision accuracy under incomplete information. The framework offers greater flexibility than classical and fuzzy graph models and can be applied to various intelligent decisionsupport systems.