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An Expert Fitness Diagnosis System Based on Elastic Cloud Computing
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-02
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
This paper presents an expert diagnosis system based on cloud computing.
It classifies a user’s fitness level based on supervised machine learning techniques.
This system is able to learn and make customized diagnoses according to the user’s physiological data, such as age, gender, and body mass index (BMI).
In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically.
It predicts the required resources in the future according to the exponential moving average of past observations.
The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.
American Psychological Association (APA)
Tseng, Kevin C.& Wu, Chia-Chuan. 2014. An Expert Fitness Diagnosis System Based on Elastic Cloud Computing. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1051855
Modern Language Association (MLA)
Tseng, Kevin C.& Wu, Chia-Chuan. An Expert Fitness Diagnosis System Based on Elastic Cloud Computing. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1051855
American Medical Association (AMA)
Tseng, Kevin C.& Wu, Chia-Chuan. An Expert Fitness Diagnosis System Based on Elastic Cloud Computing. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1051855
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051855