Developing non-parametric density estimation on genetic evolution computing as a cloud based sensor fusion method: Taking psychiatric major depressive disorder detection as an application example

Biomedical cloud computing offers on-demand healthcare services. A sensor fusion method is developed based on non-parametric density estimation on genetic evolution computing. Our method provides a potential solution for decision making on flicking features when not all measurements of sensors appea...

Full description

Saved in:
Bibliographic Details
Main Authors: Tsu-Wang Shen, Fang-Chih Liu, Chen, William Shao-Tsu
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Biomedical measurement
Cloud computing
Depressive disorder
Estimation
Genetic algorithms
Genetic Evolution
Medical diagnostic imaging
Non-Parametric Density Estimation
Sensor Fusion
Support vector machines
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Biomedical cloud computing offers on-demand healthcare services. A sensor fusion method is developed based on non-parametric density estimation on genetic evolution computing. Our method provides a potential solution for decision making on flicking features when not all measurements of sensors appear at the input end. The method was applied on major depressive disorder detection as an application example and it was successfully for MDD classification regardless different combinations of sensor monitoring.
ISSN:2156-8065
2156-8073
DOI:10.1109/ICSensT.2013.6727616