Driver drowsiness detection model identification with Bayesian network structure learning method

2016 ◽  
Author(s):  
Wenqiang Guo ◽  
Baorong Zhang ◽  
Lingjun Xia ◽  
Shuai Shi ◽  
Xiao Zhang ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Model Identification ◽  
Learning Method ◽  
Drowsiness Detection ◽  
Detection Model ◽  
Driver Drowsiness ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

scholarly journals Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Jianxiao Liu ◽  
Zonglin Tian
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Learning Method ◽  
Dependency Analysis ◽  
Learning Methods ◽  
Network Learning ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Three Phase

Background and Objective. Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. Methods. On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits. Results. In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines. Conclusions. The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

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