Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm
This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO₂, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R²). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R² of 0.9981.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © 2021 Chew Cheik Goh, et al.
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Authors:
- Goh, Chew Cheik
- Kamarudin, Latifah Munirah
- Zakaria, Ammar
- Nishizaki, Hiromitsu
- Ramli, Nuraminah
- Mao, Xiaoyang
- Syed Zakaria, Syed Muhammad
- Kanagaraj, Ericson
- Abdull Sukor, Abdul Syafiq
- Elham, Md. Fauzan
- Publication Date: 2021-8
Language
- English
Media Info
- Media Type: Print
- Pagination: 4956
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Serial:
- Sensors
- Volume: 21
- Issue Number: 15
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Air quality management; Cloud computing; Machine learning; Mathematical prediction; Monitoring; Vehicle electronics
- Subject Areas: Data and Information Technology; Environment; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01779539
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 24 2021 11:16AM