Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics

Criminal Statistics Analyzing Based on Deep Learning Methods

Authors
Xuepeng Huang, Jianhui Lin
Corresponding Author
Xuepeng Huang
Available Online August 2015.
DOI
10.2991/msam-15.2015.83How to use a DOI?
Keywords
criminal statistical data; anomaly value; deep learning; transfer delay
Abstract

Social Security Administration analysis is studied based on intensive probe to present social fluctuation researches. A complex social system model is constructed by profile vector of complex system. According to the character of the complex social system, a simplified algorithm based on deep learning is proposed, which contains a system anomaly testing expression and a corresponding referenced anomaly testing expression according to the stochastically transfer delay of the complex system. Prototype system and experiment results show the precision and credibility of the social system anomaly index obtained from the algorithm.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics
Series
Advances in Intelligent Systems Research
Publication Date
August 2015
ISBN
10.2991/msam-15.2015.83
ISSN
1951-6851
DOI
10.2991/msam-15.2015.83How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xuepeng Huang
AU  - Jianhui Lin
PY  - 2015/08
DA  - 2015/08
TI  - Criminal Statistics Analyzing Based on Deep Learning Methods
BT  - Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics
PB  - Atlantis Press
SP  - 369
EP  - 373
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-15.2015.83
DO  - 10.2991/msam-15.2015.83
ID  - Huang2015/08
ER  -