@inproceedings{78d3651e9ab24daaa9d0c9682b1905ff,
title = "Information extraction methodology by web scraping for smart cities: Using machine learning to train air quality monitor for smart cities",
abstract = "This paper presents an opportunistic sensing system for air quality monitoring to forecast the implicit factors of air pollution. Opportunistic sensing is performed by web scraping in the social network service to extract information. The data source for the air quality analysis combines two types of information: explicit and implicit information. The objective is to develop the information extraction methodology by web scraping for smart cities. The application development methodology has potential for solving real-world problems such as air pollution by data comparison between social activity observing and data collecting in sensor network.",
author = "Chung, {Chia Chun} and Jeng, {Tay Sheng}",
note = "Funding Information: This work was supported by MOST, grant No. MOST 105-2221-E-006 -029 -MY2 Publisher Copyright: {\textcopyright} 2018 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) in Hong Kong.; 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018 ; Conference date: 17-05-2018 Through 19-05-2018",
year = "2018",
language = "English",
series = "CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting",
publisher = "The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)",
pages = "515--524",
editor = "Suleiman Alhadidi and Tomohiro Fukuda and Weixin Huang and Patrick Janssen and Kristof Crolla",
booktitle = "CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia",
}