Artificial Intelligence-Driven Smart Scenic Management: Automated Decision Making and Optimization
Keywords:
smart scenic management; tourist scenic spot; artificial intelligence; automated decision making; feature optimizationAbstract
With the continuous progress of technology and economic development, the overall quality of human life has witnessed significant enhancements. Smart tourism, leveraging information technology, plays a crucial role in integrating tourism resources to offer tailored travel solutions for visitors. This approach ensures that tourists can access convenient services, such as traffic inquiries, browsing information about tourist scenic spots, and efficient route planning. As the representation of tourist information increasingly relies on visual content, particularly images, rather than textual descriptions, a challenge arises for tourists who wish to explore further details about attractions depicted in pictures. To address this issue and elevate the overall tourist experience, this study proposes an artificial intelligence-driven smart scenic management model. The model employs the modified golden jackal optimization (MGJO) algorithm for feature extraction and optimization, aiming to select the most optimal features from pool of possibilities. Additionally, the deep multi-layer recurrent neural network (DM-RNN) is utilized for the detection of tourist scenic spots, enhancing detection accuracy. A dataset of tourist-friendly scenic spots in Hsinchu City, Taiwan, is used as an example to demonstrate the effectiveness of the proposed model. The executed vacationer beautiful spot acknowledgment model effectively distinguishes 28 places of interest in Hsinchu. The experimental results demonstrate that the model that makes use of DM-RNN is both effective and precise. In terms of accuracy and mean average precision, the model performs better than previous cutting-edge models.
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