Application of Computerized Sensor Fusion Algorithm in Big Data Rural Tourism Management Considering

Abstract

This study investigates the application of computerized sensor fusion algorithms and Bayesian inference in big data rural tourism management. By integrating data from diverse sources, including environmental sensors, infrastructure monitors, and visitor interaction platforms, this research aims to provide insights into visitor demographics, preferences, environmental conditions, and infrastructure usage patterns in rural tourism destinations. The study employs computerized sensor fusion algorithms to integrate heterogeneous data streams and Bayesian inference techniques to analyze the fused dataset, deriving actionable insights for destination management and visitor experience enhancement. Statistical analysis of the integrated dataset reveals important trends regarding visitor demographics, preferences, and behaviour, as well as correlations between environmental conditions and visitation patterns. The findings underscore the significance of data-driven decision-making in rural tourism management, where insights derived from sensor data and probabilistic reasoning frameworks can inform strategic planning, marketing strategies, and infrastructure development efforts. By leveraging advanced technologies and data analytics approaches, rural destinations can optimize resource allocation, enhance visitor experiences, and promote sustainable development practices, ultimately contributing to the long-term socio-economic vitality of rural communities.