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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (10): 46-53    DOI: 10.11925/infotech.2096-3467.2018.0812
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Early Warning for Civil Aviation Security Checks Based on Deep Learning
Wengang Feng1,2,3(),Jing Huang2,4
1School of Criminal Investigation and Counter Terrorism, People’s Public Security University of China, Beijing 100038, China
2Research Centre for Civil Aviation Security, People’s Public Security University of China, Beijing 100038, China
3Police Science Postdoctoral Research Station, People’s Public Security University of China, Beijing 100038, China
4Public Security Bureau, Civil Aviation Administration of China, Beijing 100710, China
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Abstract  

[Objective]This paper proposes a hierarcical classification screening method, aiming to improve the airport security system and passenger experience. [Methods] We proposed a feature deep learning method based on the civil aviation and public security databases. Then, we trained the deep neural network of three layers: time seriers, space seriers, and environmental features to obtain the joint representing feature of passenger risk factors. Finally, we generated the early warning models for passenger security check. [Results] The proposed early warning models could relieve the pressure of civil aviation security check. [Limitaions] More research is needed to examine the proposed model with data from small airports. [Conclusions] The early-warning model based on deep learning could effectively improve work efficiency of airport security checks and passanger experience.

Key wordsCivil Aviation Security      Questionnaire Survey      Deep Learning      Flight Warning     
Received: 22 July 2018      Published: 12 November 2018

Cite this article:

Wengang Feng,Jing Huang. Early Warning for Civil Aviation Security Checks Based on Deep Learning. Data Analysis and Knowledge Discovery, 2018, 2(10): 46-53.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0812     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I10/46

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