Research on the College Students’ Physique Test Based on Artificial Neural Network and Support Vector Machines Approach

2014 ◽  
Vol 945-949 ◽  
pp. 3558-3561
Author(s):  
Han Sheng Liu
Keyword(s):  
Neural Network ◽  
College Students ◽  
Artificial Neural Network ◽  
Support Vector Machines ◽  
Support Vector ◽  
Test Management ◽  
Vector Machines ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Health Test

In recent years, China’s college students’ physique presents trend of declining. In the colleges and universities health test management, it produces the problems of only paying high attention to the test and evaluation but neglects the link of feedback and improvement. Based on artificial neural network (ANN) and support vector machines (SVM) approach, this paper makes use of the principle component analysis to conduct discussion on China's college students’ physique health test management mechanism design so as to further perfect current procedure of China's college students’ physique health test work, improve the quality of management work, improve the college students’ physique and truly reach the target for the college students’ overall development.

scholarly journals Intelligent Fish Freshness Assessment

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Hamid Gholam Hosseini ◽  
Dehan Luo ◽  
Guanggui Xu ◽  
Hongxiu Liu ◽  
Deena Benjamin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machines ◽  
Fish Species ◽  
Support Vector ◽  
Intelligent Processing ◽  
Fish Species Identification ◽  
Vector Machines ◽  
Industry Applications ◽  
Artificial Neural Network Ann

Fish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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