Exploring Uncertain Data with Fuzzy Logic in Cultural Heritage Conservation

Abstract

Cultural heritage conservation has faced serious problems in addressing uncertainty and imprecise data through the years, so this research basically proposes a fuzzy logic-based framework to deal with these challenges. This is because traditional decision-making models often do not have in built mechanisms to handle the diversity and complexity of environmental factors (such as relative humidity, temperature) or material properties which determine whether a heritage object will survive over time. This work is an application of fuzzy sets, inference rules and defuzzification techniques to evaluate death status or preservation actions for artifacts.

Using mathematical modelling and a case study on museum artifacts, the research shows that fuzzy logic provides greater accuracy as well adaptability in conservation decisions. This work has produced a toolbox for exploring FISs in SEA systems and we have leveraged its tools to demonstrate the capabilities of our proposed framework based upon max-min aggregation with centroid-based defuzzification that illustrates scalability across multiple conservation scenarios. Results evaluate its general performance, supported by it achieving an F-measure of above 0.842 for complex-structured input data in the absence of consent certainty.

The route for future works includes real-time monitoring by integrating the model with IoT sensors, developing hybrid systems of fuzzy logic and machine learning algorithms to solve other heritage questions, crowd dynamics at huge cultural sites. This study highlights how state-of-the-art computational techniques can help protect cultural heritage artifacts and monuments from deterioration for centuries.