Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77286
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dc.contributor.authorWang, Ziao
dc.date.accessioned2019-05-24T02:44:47Z
dc.date.available2019-05-24T02:44:47Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77286
dc.description.abstractArtificial Intelligence Monitoring at the Edge basically monitors the urban noise classes and sound pressure level in Singapore urban area. In this project, I processed raw Singapore urban noise data collected from Yuhua garden and trained the classifier for 8 different classes of Singapore urban noise with processed noise data using machine learning method. Five different machine learning models are used to train the classifier and their performance are compared to find out the optimizing model under this noise classification scenario.en_US
dc.format.extent55 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleArtificial intelligence monitoring at the edge for smart nation deploymenten_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorGan Woon Sengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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