VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/283
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Huynh Trung Hieu | en_US |
dc.contributor.author | Ho Dac Quan | en_US |
dc.contributor.author | Yonggwan Won | en_US |
dc.date.accessioned | 2020-03-12T16:58:18Z | - |
dc.date.available | 2020-03-12T16:58:18Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://epub.vgu.edu.vn/handle/dlibvgu/283 | - |
dc.description.abstract | In 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 approach | en_US |
dc.language.iso | en | en_US |
dc.subject | Glucose measurement | en_US |
dc.subject | Hematocrit | en_US |
dc.subject | Handheld device | en_US |
dc.subject | Neural network | en_US |
dc.title | Accuracy improvement for glucose measurement in handheld devices by using neural networks | en_US |
dc.type | Article | en_US |
dc.relation.conference | International Conference on Future Data and Security Engineering | en_US |
dc.relation.duration | 29/11-1/12/2017 | en_US |
dc.relation.conferencevenue | Ho Chi Minh City, Viet Nam | en_US |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | other | - |
Appears in Collections: | Conference papers |
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