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 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.
Product Design and Development Laboratory, Department of Industrial Design, College of Management, Chang Gung University, 259 Wenhua 1st Road, Guishan Shiang, Taoyuan 33302, Taiwan ; Healthy Aging Research Center, Chang Gung University, Taiwan.
Kevin C
KC
Tseng
Product Design and Development Laboratory, Department of Industrial Design, College of Management, Chang Gung University, 259 Wenhua 1st Road, Guishan Shiang, Taoyuan 33302, Taiwan.
Chia-Chuan
CC
Wu
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology; D016247:Information Storage and Retrieval; D012984:Software