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doi: 10.3934/dcdss.2019095

Intelligent recognition algorithm for social network sensitive information based on classification technology

1. 

School of Information Engineering, Wuhan University of Technology, Wuhan, China

2. 

Department of Police Technology, Railway Police College, Zhengzhou, China

* Corresponding author: Weiping Li

Received  June 2017 Revised  December 2017 Published  November 2018

In the social network, there is the problem of network sensitive information with low accuracy rate of information recognition. To effectively improve the accuracy of intelligent identification of sensitive information, an intelligent recognition algorithm for sensitive information based on improved fuzzy support vector machine is proposed in this paper. The information is collected. The trajectory of the best movement of the information node is found in the low energy cache. In the limited time, the performance of information acquisition is improved by using the mobility of information nodes. According to DFS criterion, the features are added into the feature subset or eliminate the sensitive information. The feature selection algorithm based on multi-label is applied to feature selection of the collected information, so that the information gain between information feature and label set can be used to measure the importance. The improved support vector machine classification algorithm is used to classify the information selected by feature selection, and select effective candidate support vector, reduce the number of training samples, and improve the training speed. The new membership function is defined to enhance the effect of support vector on the construction of fuzzy support vector machine. Finally, the nearest neighbor sample density is applied to the design of membership function to reduce the noise, and achieve intelligent recognition of the sensitive information in the social network. Experimental results show that the accuracy rate of sensitive information intelligent recognition can be effectively improved by using the proposed algorithm.

Citation: Weiping Li, Haiyan Wu, Jie Yang. Intelligent recognition algorithm for social network sensitive information based on classification technology. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2019095
References:
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[2]

C.-C. ChungW.-Y. DzanY.-M. Cheng and S.-J. Lou, On the push-pull mobile learning of electric welding., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 3235-3260.

[3]

X. Du, Target recognition algorithm for fused hyperspectral image by using combined spectra, Spectroscopy Letters, 48 (2015), 251-258.

[4]

X. Fan, K. Zheng, Y. Zhou and S. Wang, Pose locality constrained representation for 3d human pose reconstruction, Journal of Jilin University (Information Science Edition), 1-7.

[5]

W. GaoL. ZhuY. Guo and K. Wang, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent & Fuzzy Systems, 33 (2017), 3153-3163.

[6]

J. HouZ. C. Wen and J. F. Lai, A constrained optimization reformulation of the generalized nash equilibrium problem, Journal of Interdisciplinary Mathematics, 20 (2017), 27-34.

[7]

Z. Huang, Improved adaboost detection algorithm and application in identity authentication, Bulletin of Science and Technology, 190-192.

[8]

K. Kahl and J. S. Sirkis, Damage detection in beam structures using subspace rotation algorithm with strain data, Aiaa Journal, 34 (2015), 2609-2614.

[9]

K. KimS. H. Kong and S. Y. Jeon, Slip and slide detection and adaptive information sharing algorithms for high-speed train navigation systems, IEEE Transactions on Intelligent Transportation Systems, 51 (2015), 3193-3203.

[10]

S. G. KonovA. A. Khokholikov and V. V. Skvortsova, Algorithm for rapid recognition of measurement markers for non-contact measurement systems, Measurement Techniques, 58 (2015), 845-847.

[11]

A. Leon M.L. David J.W. Steven C.R.J. MarthaM. ChristophS. JohannesJ. Karl-HeinzW. Christian and M. Andre F., Making use of longitudinal information in pattern recognition, Human Brain Mapping, 72 (2016), 4385-4404.

[12]

B. LiY. Tang and T. Han, Research on human face recognition based on improved nmf algorithm, Computer Simulation, 3 (2016), 428-432.

[13]

H. LimT. Park and N. S. Kim, Joint optimisation of computational accuracy and algorithm parameters for energy-efficient recognition algorithms, Electronics Letters, 51 (2015), 1238-1240.

[14]

A. K. Malhi and S. Batra, An efficient certificateless aggregate signature scheme for vehicular ad-hoc networks, Discrete Mathematics and Theoretical Computer Science, 17 (2015), 317-338.

[15]

G. NapolitanoA. MarshallP. Hamilton and A. T. Gavin, Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction, Artificial Intelligence in Medicine, 70 (2016), 77-83.

[16]

G. X. RitterJ. A. Nieves-Vázquez and G. Urcid, A simple statistics-based nearest neighbor cluster detection algorithm, Pattern Recognition, 48 (2015), 918-932.

[17]

D. Shashikumar and S. Srinivas, 3d human activity recognition by indexing and sequencing (risq), Nature, 367 (2015), 480-483.

[18]

C. SzabóL. C. MorganK. M. KarkarL. D. LearyO. V. LieM. Girouard and J. E. Cavazos, Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-eeg recordings, Epilepsia, 56 (2015), 1432-1437.

[19]

D. Wang, H. Lu and M. H. Yang, Kernel Collaborative Face Recognition, 10, Elsevier Science Inc., 2015.

[20]

H. Wang, X. Su, X. Lu and M. Cao, Based on the improved grey relational algorithm platform for the airborne radar emitter recognition method, Journal of China Academy of Electronics and Information Technology, 523-526.

[21]

Y. Wei, Assessment study on brain wave predictive ability to policemens safety law enforcement, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 193-204.

[22]

Y. XuZ. LiB. ZhangJ. Yang and J. You, Sample diversity, representation effectiveness and robust dictionary learning for face recognition, Information Sciences, 375 (2017), 171-182.

[23]

G. Zengtai and W. Qian, On the connection of fuzzy hypergraph with fuzzy information system, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 33, 1665-1676.

[24]

X. ZhangC. MeiD. Chen and J. Li, Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy, Pattern Recognition, 56 (2016), 1-15.

show all references

References:
[1]

M. Abdelwahab and M. Abdelwahab, Human action recognition and analysis algorithm for fixed and moving cameras, Electronics Letters, 23 (2015), 1869-1871.

[2]

C.-C. ChungW.-Y. DzanY.-M. Cheng and S.-J. Lou, On the push-pull mobile learning of electric welding., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 3235-3260.

[3]

X. Du, Target recognition algorithm for fused hyperspectral image by using combined spectra, Spectroscopy Letters, 48 (2015), 251-258.

[4]

X. Fan, K. Zheng, Y. Zhou and S. Wang, Pose locality constrained representation for 3d human pose reconstruction, Journal of Jilin University (Information Science Edition), 1-7.

[5]

W. GaoL. ZhuY. Guo and K. Wang, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent & Fuzzy Systems, 33 (2017), 3153-3163.

[6]

J. HouZ. C. Wen and J. F. Lai, A constrained optimization reformulation of the generalized nash equilibrium problem, Journal of Interdisciplinary Mathematics, 20 (2017), 27-34.

[7]

Z. Huang, Improved adaboost detection algorithm and application in identity authentication, Bulletin of Science and Technology, 190-192.

[8]

K. Kahl and J. S. Sirkis, Damage detection in beam structures using subspace rotation algorithm with strain data, Aiaa Journal, 34 (2015), 2609-2614.

[9]

K. KimS. H. Kong and S. Y. Jeon, Slip and slide detection and adaptive information sharing algorithms for high-speed train navigation systems, IEEE Transactions on Intelligent Transportation Systems, 51 (2015), 3193-3203.

[10]

S. G. KonovA. A. Khokholikov and V. V. Skvortsova, Algorithm for rapid recognition of measurement markers for non-contact measurement systems, Measurement Techniques, 58 (2015), 845-847.

[11]

A. Leon M.L. David J.W. Steven C.R.J. MarthaM. ChristophS. JohannesJ. Karl-HeinzW. Christian and M. Andre F., Making use of longitudinal information in pattern recognition, Human Brain Mapping, 72 (2016), 4385-4404.

[12]

B. LiY. Tang and T. Han, Research on human face recognition based on improved nmf algorithm, Computer Simulation, 3 (2016), 428-432.

[13]

H. LimT. Park and N. S. Kim, Joint optimisation of computational accuracy and algorithm parameters for energy-efficient recognition algorithms, Electronics Letters, 51 (2015), 1238-1240.

[14]

A. K. Malhi and S. Batra, An efficient certificateless aggregate signature scheme for vehicular ad-hoc networks, Discrete Mathematics and Theoretical Computer Science, 17 (2015), 317-338.

[15]

G. NapolitanoA. MarshallP. Hamilton and A. T. Gavin, Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction, Artificial Intelligence in Medicine, 70 (2016), 77-83.

[16]

G. X. RitterJ. A. Nieves-Vázquez and G. Urcid, A simple statistics-based nearest neighbor cluster detection algorithm, Pattern Recognition, 48 (2015), 918-932.

[17]

D. Shashikumar and S. Srinivas, 3d human activity recognition by indexing and sequencing (risq), Nature, 367 (2015), 480-483.

[18]

C. SzabóL. C. MorganK. M. KarkarL. D. LearyO. V. LieM. Girouard and J. E. Cavazos, Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-eeg recordings, Epilepsia, 56 (2015), 1432-1437.

[19]

D. Wang, H. Lu and M. H. Yang, Kernel Collaborative Face Recognition, 10, Elsevier Science Inc., 2015.

[20]

H. Wang, X. Su, X. Lu and M. Cao, Based on the improved grey relational algorithm platform for the airborne radar emitter recognition method, Journal of China Academy of Electronics and Information Technology, 523-526.

[21]

Y. Wei, Assessment study on brain wave predictive ability to policemens safety law enforcement, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 193-204.

[22]

Y. XuZ. LiB. ZhangJ. Yang and J. You, Sample diversity, representation effectiveness and robust dictionary learning for face recognition, Information Sciences, 375 (2017), 171-182.

[23]

G. Zengtai and W. Qian, On the connection of fuzzy hypergraph with fuzzy information system, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 33, 1665-1676.

[24]

X. ZhangC. MeiD. Chen and J. Li, Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy, Pattern Recognition, 56 (2016), 1-15.

Figure 1.  Information node access
Figure 2.  Preselected effective support vector
Figure 3.  Membership function based on class center
Figure 4.  Classification result obtained with the current method
Figure 5.  The proposed algorithm
Figure 6.  Comparison of information recognition accuracy rate between different methods
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