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Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security

Author

Listed:
  • Fathe Jeribi

    (College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia)

  • Shaik Rafi Ahamed

    (Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India)

  • Uma Perumal

    (College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia)

  • Mohammed Hameed Alhameed

    (College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia)

  • Manjunatha Chari Kamsali

    (Department of EECE, GITAM University, Hyderabad 530045, India)

Abstract

Cultural tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for peak visitor time in Riyadh, a city in Saudi Arabia. The main objective of the framework is to improve the cultural tourism of Riyadh by considering various factors to help in improving CT based on recommendation system (RS). Primarily, the map data and cultural event dataset were processed for location, such as grouping with Kriging interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes were processed with word embedding. Meanwhile, the social network sites (SNS) data like reviews and news were extracted with an external application programming interface (API). The review data were processed with keyword extraction and word embedding, whereas the news data were processed with score value estimation. Lastly, the data were fused, corresponding to a historical site, and given to the Multi-Quadratic-Long Short-Term Memory (MQ-LSTM) recommendation system (RS); also, the recommended result with the map was stored in a database. Lastly, the database security was maintained with locality sensitive hashing (LSH). From the experimental evaluation with multiple databases including the Riyadh Restaurants 20K dataset, the proposed recommendation model achieved a recommendation rate (RR) of 97.22%, precision of 97.7%, recall of 98.27%, and mean absolute error (MAE) of 0.0521. This result states that the proposed RS provides higher RR and reduced error compared to existing related RSs. Thus, by attaining higher performance values, the proposed model is experimentally verified.

Suggested Citation

  • Fathe Jeribi & Shaik Rafi Ahamed & Uma Perumal & Mohammed Hameed Alhameed & Manjunatha Chari Kamsali, 2023. "Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security," Sustainability, MDPI, vol. 15(23), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16276-:d:1287122
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    References listed on IDEAS

    as
    1. Hong Li & Man Qiao & Shuai Peng & Wei Zhang, 2022. "Research on the Recommendation Algorithm of Rural Tourism Routes Based on the Fusion Model of Multiple Data Sources," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-10, April.
    2. Zheng Cao & Heng Xu & Brian Sheng-Xian Teo, 2023. "Sentiment of Chinese Tourists towards Malaysia Cultural Heritage Based on Online Travel Reviews," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    3. Shengyi Yang & Wen-Tsao Pan, 2022. "Analytic Hierarchy Process and Its Application in Rural Tourism Service Performance Evaluation," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-10, June.
    4. Pei Ling Sung & Teng Yuan Hsiao & Leo Huang & Alastair M. Morrison, 2021. "The influence of green trust on travel agency intentions to promote low‐carbon tours for the purpose of sustainable development," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(4), pages 1185-1199, July.
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