Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/65510
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dc.contributor.authorKrittakom Srijiranonen_US
dc.contributor.authorNarissara Eiamkanitchaten_US
dc.date.accessioned2019-08-05T04:34:36Z-
dc.date.available2019-08-05T04:34:36Z-
dc.date.issued2019-05-10en_US
dc.identifier.other2-s2.0-85066472403en_US
dc.identifier.other10.1109/ICSEC.2018.8712693en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066472403&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/65510-
dc.description.abstract© 2018 IEEE. Air contamination is one of the primary issues in the world. PM10 is the major pollutant having highly affecting in human wellbeing. Numerous scientists around the world create many classifications and prediction model to conjecture PM10 for alarm people in their country. Consistently from February to May in the northern part of Thailand, there is the exhaust cloud issue of PM10 yet there are few pieces of research in the air pollution utilizing the up to date data set. By observing this issue, refreshed information between 2011 and 2017 are utilized. Only two of the data from stations in Lampang and Phayao were selected for this study. Due to the minimal data loss problem. The collective neural networks system is selected to create an appropriate classification model. The average accuracy of prediction results in this work is 92.51% which higher than related works in a similar topic.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectMathematicsen_US
dc.titleCollective neural networks system for PM<inf>10</inf> classification in the north of Thailanden_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2018 22nd International Computer Science and Engineering Conference, ICSEC 2018en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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