@inproceedings{
 title = {Air Quality Monitoring Intelligent System Using Machine Learning Techniques},
 type = {inproceedings},
 year = {2018},
 keywords = {Air quality,Intelligent system,Monitoring system},
 pages = {75-80},
 websites = {https://ieeexplore.ieee.org/document/8564511/},
 month = {11},
 publisher = {IEEE},
 id = {f2f8d727-77b8-3a7b-af89-2f453d149af2},
 created = {2022-01-26T03:00:47.076Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:00:47.076Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Rosero-Montalvo2018},
 private_publication = {false},
 abstract = {Environment monitoring is so important because it is based on the first right of people, life and health. For this reason, this system monitoring air quality with different sensor nodes in the Ibarra that evaluate the parameters of CO2, NOx, UV Light, Temperature and Humidity. The data analysis through machine learning algorithms allow the system to classify autonomously if a certain geographical location is exceeding the established emission limits of gases. As a result, the k-Nearest Neighbor algorithm presented a great classification performance when selecting the most contaminated sectors.},
 bibtype = {inproceedings},
 author = {Rosero-Montalvo, Paul D. and Caraguay-Procel, Jorge A. and Jaramillo, Edgar D. and Michilena-Calderon, Jaime M. and Umaquinga-Criollo, Ana C. and Mediavilla-Valverde, Mario and Ruiz, Miguel A. and Beltran, Luis A. and Peluffo, Diego H.},
 doi = {10.1109/INCISCOS.2018.00019},
 booktitle = {2018 International Conference on Information Systems and Computer Science (INCISCOS)}
}

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