An Expert Fitness Diagnosis System Based on Elastic Cloud Computing
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 ind...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/981207 |
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| _version_ | 1849306282657316864 |
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| author | Kevin C. Tseng Chia-Chuan Wu |
| author_facet | Kevin C. Tseng Chia-Chuan Wu |
| author_sort | Kevin C. Tseng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-382aa3a1eb2e43bd99670cc6b3083673 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-382aa3a1eb2e43bd99670cc6b30836732025-08-20T03:55:11ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/981207981207An Expert Fitness Diagnosis System Based on Elastic Cloud ComputingKevin C. Tseng0Chia-Chuan Wu1Product Design and Development Laboratory, Department of Industrial Design, College of Management, Chang Gung University, 259 Wenhua 1st Road, Guishan Shiang, Taoyuan 33302, TaiwanProduct Design and Development Laboratory, Department of Industrial Design, College of Management, Chang Gung University, 259 Wenhua 1st Road, Guishan Shiang, Taoyuan 33302, TaiwanThis 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.http://dx.doi.org/10.1155/2014/981207 |
| spellingShingle | Kevin C. Tseng Chia-Chuan Wu An Expert Fitness Diagnosis System Based on Elastic Cloud Computing The Scientific World Journal |
| title | An Expert Fitness Diagnosis System Based on Elastic Cloud Computing |
| title_full | An Expert Fitness Diagnosis System Based on Elastic Cloud Computing |
| title_fullStr | An Expert Fitness Diagnosis System Based on Elastic Cloud Computing |
| title_full_unstemmed | An Expert Fitness Diagnosis System Based on Elastic Cloud Computing |
| title_short | An Expert Fitness Diagnosis System Based on Elastic Cloud Computing |
| title_sort | expert fitness diagnosis system based on elastic cloud computing |
| url | http://dx.doi.org/10.1155/2014/981207 |
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