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Tourism Information Management System Using Neural Networks Driven by Particle Swarm Model
Xuan Gao1,2, Yuan Qi2,3, Yong Chai1,2
1School of International Hospitality Management, University of Sanya, Sanya 572022, China.
Computational Intelligence and Neuroscience
|June 23, 2022
Summary
This study introduces a smart tourism management system using a PSO-optimized Neural Network (NN). The system enhances user experience with personalized recommendations, achieving 94.67% prediction accuracy and 96.11% recall.
Area of Science:
- Information Science
- Computer Science
- Tourism Management
Background:
- Traditional tourism management systems lack personalization and efficiency.
- The concept of smart tourism necessitates advanced information systems for enhanced user experience and operational effectiveness.
Purpose of the Study:
- To design and implement an intelligent tourism management information system.
- To enhance user experience through personalized search results and recommendations.
- To improve tourism management strategies using data analysis and predictive algorithms.
Main Methods:
- Development of a tourism management system utilizing a Particle Swarm Optimization (PSO)-optimized Neural Network (NN).
- Implementation of a web front-end using DIV+CSS, PHP, and an SQL Server database for user data management.
- Integration of personalized components to tailor search ranking results based on user consumption habits.
Main Results:
- The developed system achieved a prediction accuracy of 94.67%.
- The system demonstrated a recall rate of 96.11%.
- Experimental results indicate superior performance compared to traditional algorithms.
Conclusions:
- The PSO-optimized NN approach offers significant improvements over conventional methods in tourism management.
- The system effectively promotes the transformation and upgrading of the tourism industry structure.
- This smart tourism system enhances the overall development level of the tourism industry through informatization.
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