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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/283
DC FieldValueLanguage
dc.contributor.authorHuynh Trung Hieuen_US
dc.contributor.authorHo Dac Quanen_US
dc.contributor.authorYonggwan Wonen_US
dc.date.accessioned2020-03-12T16:58:18Z-
dc.date.available2020-03-12T16:58:18Z-
dc.date.issued2017-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/283-
dc.description.abstractIn this study, an approach for improving the accuracy of glucose measurement by using handheld devices is presented. The proposed approach is based on reducing the effects of hematocrit. The hematocrit is estimated by using a neural network which is trained by a non-iterative learning algorithm. The inputs for neural network are sampled from the transduced current curve. This current curve is generated during the chemical reactions of glucose measurement process in the handheld devices. The experiments performed on a real dataset show that the accuracy of glucose measurement using the handheld devices is improved by using the proposed approachen_US
dc.language.isoenen_US
dc.subjectGlucose measurementen_US
dc.subjectHematocriten_US
dc.subjectHandheld deviceen_US
dc.subjectNeural networken_US
dc.titleAccuracy improvement for glucose measurement in handheld devices by using neural networksen_US
dc.typeArticleen_US
dc.relation.conferenceInternational Conference on Future Data and Security Engineeringen_US
dc.relation.duration29/11-1/12/2017en_US
dc.relation.conferencevenueHo Chi Minh City, Viet Namen_US
item.languageiso639-1other-
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:Conference papers
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