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Neutrosophic Sets and Systems

Abstract

This study addresses the phenomenon of prioritization of risks during large-scale audits as risks evident in the complicated environment and uncertain information are challenging to mitigate. The importance of the topic is that for complicated entities with high uncertainty, high impact, and low/high probability of audit detection, proper allocation in this resource sensitive 21st century is needed for effective audit. The literature demonstrates means to assess and rank risk for subsequent corrective actions that traditional statistical methods cannot capture. The developed hybrid model combines Random Forest, fuzzy logic, and neutrosophic logic are used to process homicide data, demographic variables, and socioeconomic factors in Ecuador. The results demonstrate % accuracy in predicting security levels by canton, significantly outperforming traditional deterministic approaches. The developed platform generates interactive, georeferenced visualizations that facilitate understanding of risk patterns and support informed decision-making in citizen security policies. This research contributes to the development of more robust and adaptive predictive systems, establishing a methodological precedent for the application of neutrosophics to public security and social risk management issues.

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