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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/102
DC FieldValueLanguage
dc.contributor.authorHuynh Trung Hieuen_US
dc.contributor.authorYonggwan Wonen_US
dc.date.accessioned2020-03-12T16:55:11Z-
dc.date.available2020-03-12T16:55:11Z-
dc.date.issued2019-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/102-
dc.description.abstractGlucose measurement by using handheld devices is applied widely due to their comfortabilities. They are easy to use and can give results quickly. However, the accuracy of measurement results is affected by interferences, in which hematocrit (HCT) is one of the most highly affecting factors. In this paper, an approach for glucose correction based on the neural network is presented. The regularized online sequential learning is utilized for hematocrit estimation. The transduced current curve which is produced by the chemical reaction during glucose measurement is used as an input feature of neural network. The experimental results shown that the proposed approach is promising. page 160-171en_US
dc.language.isoenen_US
dc.publisherSpringer Linken_US
dc.relation.ispartofseriesLecture Notes in Computer Science;Vol. 11390-
dc.subjectHematocriten_US
dc.subjectNeural networken_US
dc.subjectOnline trainingen_US
dc.subjectGlucose correctionen_US
dc.subjectHandheld deviceen_US
dc.titleApplication of regularized online sequential learning for glucose correctionen_US
dc.identifier.doi10.1007/978-3-662-58808-6_7-
dc.relation.journaltypeScopus (Q2)en_US
item.languageiso639-1other-
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:ARTICLE/BOOK PUBLICATION
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