• Previous Article
    The optimization algorithm for blind processing of high frequency signal of capacitive sensor
  • DCDS-S Home
  • This Issue
  • Next Article
    An optimization detection algorithm for complex intrusion interference signal in mobile wireless network
August & September  2019, 12(4&5): 1385-1398. 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, 2019, 12 (4&5) : 1385-1398. doi: 10.3934/dcdss.2019095
References:
[1]

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

[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.   Google Scholar

[3]

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

[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. Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[7]

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

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[12]

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

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[17]

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

[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.   Google Scholar

[19]

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

[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. Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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. Google Scholar

[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.   Google Scholar

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.   Google Scholar

[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.   Google Scholar

[3]

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

[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. Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[7]

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

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[12]

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

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[17]

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

[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.   Google Scholar

[19]

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

[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. Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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. Google Scholar

[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.   Google Scholar

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
[1]

Gaurav Nagpal, Udayan Chanda, Nitant Upasani. Inventory replenishment policies for two successive generations price-sensitive technology products. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021036

[2]

Juliang Zhang, Jian Chen. Information sharing in a make-to-stock supply chain. Journal of Industrial & Management Optimization, 2014, 10 (4) : 1169-1189. doi: 10.3934/jimo.2014.10.1169

[3]

F.J. Herranz, J. de Lucas, C. Sardón. Jacobi--Lie systems: Fundamentals and low-dimensional classification. Conference Publications, 2015, 2015 (special) : 605-614. doi: 10.3934/proc.2015.0605

[4]

Rui Hu, Yuan Yuan. Stability, bifurcation analysis in a neural network model with delay and diffusion. Conference Publications, 2009, 2009 (Special) : 367-376. doi: 10.3934/proc.2009.2009.367

[5]

Jingni Guo, Junxiang Xu, Zhenggang He, Wei Liao. Research on cascading failure modes and attack strategies of multimodal transport network. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2020159

[6]

Andrey Kovtanyuk, Alexander Chebotarev, Nikolai Botkin, Varvara Turova, Irina Sidorenko, Renée Lampe. Modeling the pressure distribution in a spatially averaged cerebral capillary network. Mathematical Control & Related Fields, 2021  doi: 10.3934/mcrf.2021016

[7]

Mats Gyllenberg, Jifa Jiang, Lei Niu, Ping Yan. On the classification of generalized competitive Atkinson-Allen models via the dynamics on the boundary of the carrying simplex. Discrete & Continuous Dynamical Systems - A, 2018, 38 (2) : 615-650. doi: 10.3934/dcds.2018027

[8]

Reza Lotfi, Yahia Zare Mehrjerdi, Mir Saman Pishvaee, Ahmad Sadeghieh, Gerhard-Wilhelm Weber. A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 221-253. doi: 10.3934/naco.2020023

2019 Impact Factor: 1.233

Metrics

  • PDF downloads (103)
  • HTML views (445)
  • Cited by (0)

Other articles
by authors

[Back to Top]