{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:35:06Z","timestamp":1773930906672,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643681948","type":"print"},{"value":"9781643681955","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,8]]},"abstract":"<jats:p>Accurately assessing the air quality index (AQI) values and levels has become an attractive research topic during the last decades. It is a crucial aspect when studying the possible adverse health effects associated with current air quality conditions. This paper aims to utilize machine learning and an appropriate selection of attributes for the air quality estimation problem using various features, including sensor data (humidity, temperature), timestamp features, location features, and public weather data. We evaluated the performance of different learning models and features to study the problem using the data set \u201cMNR-HCM II\u201d. The experimental results show that adopting TLPW features with Stacking generalization yields higher overall performance than other techniques and features in RMSE, accuracy, and F1-score.<\/jats:p>","DOI":"10.3233\/faia210040","type":"book-chapter","created":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T09:22:57Z","timestamp":1631784177000},"source":"Crossref","is-referenced-by-count":7,"title":["An Effective AQI Estimation Using Sensor Data and Stacking Mechanism"],"prefix":"10.3233","author":[{"given":"Dat Q.","family":"Duong","sequence":"first","affiliation":[{"name":"AISIA Research Lab"}]},{"given":"Quang M.","family":"Le","sequence":"additional","affiliation":[{"name":"AISIA Research Lab"}]},{"given":"Tan-Loc","family":"Nguyen-Tai","sequence":"additional","affiliation":[{"name":"University of Information Technology, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University in Ho Chi Minh City, Vietnam"}]},{"given":"Hien D.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Information Technology, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University in Ho Chi Minh City, Vietnam"}]},{"given":"Minh-Son","family":"Dao","sequence":"additional","affiliation":[{"name":"National Institute of Information and Communications Technology, Japan"}]},{"given":"Binh T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"AISIA Research Lab"},{"name":"University of Science, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University in Ho Chi Minh City, Vietnam"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210040","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:29:09Z","timestamp":1635168549000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210040"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"ISBN":["9781643681948","9781643681955"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210040","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,8]]}}}