Travel Explore-Based Heritage Using AI

Cultural heritage tourism is the most effective way to preserve human identity and from country-to-country exchange, but these traditional traveling history approaches have some constraints with respect of being inadequate in terms of directingiveness, personalization, and sustainability. In this paper, we introduce TravelExplore (more), a CAVE-based system designed to revolutionize heritage tourism by delivering personal digital experiences that are immersive and sustainable. By integrating natural language processing, computer vision, recommender system and predictive analysis technologies the framework is designed to facilitate visits of cultural heritage sites by increasing knowledge accessibility and dissemination and driving heritage maintenance. It combines these first-hand data sources, normally obtained from user surveys and travel logs (geotagged) or multimedia resources with second-hand data representations also received from archives of cultural heritage a tourist web site. The input then is fed to AI modules to adapt location-aware stories generation, image-based site spotting and adaptive path planning. The performance was evaluated by implementing the prototype and it demonstrated good one, high reliability of monument recognition, positive feedback from user’s side on personalized level and efficient visibility in predictive modeling of visitor flow control.