Biometric monitoring system based on K-means &MTLS-SVM algorithm

In a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervi...

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Main Authors: Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG
Format: Article
Language:zho
Published: Beijing Xintong Media Co., Ltd 2017-10-01
Series:Dianxin kexue
Subjects:
Online Access:http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2017286
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author Jingming XIA
Lingling TANG
Ling TAN
Han ZHENG
author_facet Jingming XIA
Lingling TANG
Ling TAN
Han ZHENG
author_sort Jingming XIA
collection DOAJ
description In a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method,the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.
format Article
id doaj-art-a67dcab204204dd08d933e1776b8492c
institution OA Journals
issn 1000-0801
language zho
publishDate 2017-10-01
publisher Beijing Xintong Media Co., Ltd
record_format Article
series Dianxin kexue
spelling doaj-art-a67dcab204204dd08d933e1776b8492c2025-08-20T02:09:19ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-10-0133434959599419Biometric monitoring system based on K-means &MTLS-SVM algorithmJingming XIALingling TANGLing TANHan ZHENGIn a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method,the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2017286physiological parameter;time series prediction;K-means clustering;multi-task learning
spellingShingle Jingming XIA
Lingling TANG
Ling TAN
Han ZHENG
Biometric monitoring system based on K-means &MTLS-SVM algorithm
Dianxin kexue
physiological parameter;time series prediction;K-means clustering;multi-task learning
title Biometric monitoring system based on K-means &MTLS-SVM algorithm
title_full Biometric monitoring system based on K-means &MTLS-SVM algorithm
title_fullStr Biometric monitoring system based on K-means &MTLS-SVM algorithm
title_full_unstemmed Biometric monitoring system based on K-means &MTLS-SVM algorithm
title_short Biometric monitoring system based on K-means &MTLS-SVM algorithm
title_sort biometric monitoring system based on k means mtls svm algorithm
topic physiological parameter;time series prediction;K-means clustering;multi-task learning
url http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2017286
work_keys_str_mv AT jingmingxia biometricmonitoringsystembasedonkmeansmtlssvmalgorithm
AT linglingtang biometricmonitoringsystembasedonkmeansmtlssvmalgorithm
AT lingtan biometricmonitoringsystembasedonkmeansmtlssvmalgorithm
AT hanzheng biometricmonitoringsystembasedonkmeansmtlssvmalgorithm