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dc.contributor.authorMuhammad Naufal, Mansor-
dc.contributor.authorSyahryull Hi-Fi Syam, Ahmad Jamil-
dc.contributor.authorMuhammad Nazri, Rejab-
dc.contributor.authorAddzrull Hi-Fi Syam, Ahmad Jamil-
dc.date.accessioned2013-07-10T08:15:17Z-
dc.date.available2013-07-10T08:15:17Z-
dc.date.issued2012-08-25-
dc.identifier.citationp. 374-376en_US
dc.identifier.isbn978-146732467-0-
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6324598-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/26565-
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.orgen_US
dc.description.abstractPain Assessment in Neonatal has been discussed recently nowadays. A rapid research, equipment and pain course has yet been improved. However, the robustness, accurate and fast pain scheme is yet far beyond the schedule comparing to the pain assessment for the adult patient. Thus, an infant pain detection scheme is been proposed based on Autoregressive Model (AR Model) and Fuzzy k-NN. The accuracy result is quite promising around 90.77%.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofseriesProceedings of the International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA 2012)en_US
dc.subjectAR Modelen_US
dc.subjectDetection of facial changesen_US
dc.subjectFuzzy k-NN classifieren_US
dc.subjectNICU patienten_US
dc.titleAR model for infant pain anxiety recognition using fuzzy k-NNen_US
dc.typeWorking Paperen_US
dc.contributor.urlapairia@yahoo.comen_US
dc.contributor.urlsyahrull30@yahoo.comen_US
dc.contributor.urlnazri_554@yahoo.comen_US
dc.contributor.urlazrulhifisyam@yahoo.comen_US
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