MySecureMap: A Geolocation-Based Recommender System for Safety and Healthy Environment
Issued Date
2025-01-01
Resource Type
ISSN
21593442
eISSN
21593450
Scopus ID
2-s2.0-105034080261
Journal Title
IEEE Region 10 Annual International Conference Proceedings TENCON
Start Page
1481
End Page
1485
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Region 10 Annual International Conference Proceedings TENCON (2025) , 1481-1485
Suggested Citation
Tangworakitthaworn P., Kaewpradub P., Hathaichot P., Mahasiripanya W. MySecureMap: A Geolocation-Based Recommender System for Safety and Healthy Environment. IEEE Region 10 Annual International Conference Proceedings TENCON (2025) , 1481-1485. 1485. doi:10.1109/TENCON66050.2025.11374960 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116065
Title
MySecureMap: A Geolocation-Based Recommender System for Safety and Healthy Environment
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
This research presents the MySecureMap system, a geolocation-based recommender system designed for real-time air quality visualization and safetyfocused navigation. The system leverages the Air Quality Index (AQI) data provided by both government and private sources. The proposed mechanism has integrated the Long Short-Term Memory (LSTM) neural network's machine learning technique which is capable of predicting the AQI levels and providing the route recommendations. The ultimate goal of the proposed system aims to protect users from the hazardous air pollutants by automatically identifying the risk zones and suggesting the safe routes which are visualized on the map. This paper discusses the proposed approach and the system architecture, the design and the development of the proposed MySecureMap system, and the performance evaluation, highlighting its usability and effectiveness in enhancing the environmental awareness and public health. The performance evaluation results shown that the proposed system met the acceptable accuracy with Mean absolute percent error (MAPE) of 2.39% when comparing the predicted forecast value with the real AQI data observed for the next 12 hours.