Abstract
In order to solve the phenomenon that the malicious information retrieval in the traditional method is not comprehensive and the precision is not high, an efficient retrieval method of malicious information in multimedia big data network based on human-computer interaction is proposed and designed. On the basis of analyzing the principle of information retrieval, the human-computer interaction form is used to divide the metrics of malicious information. On this basis, the human-computer interaction retrieval logic is established, and the malicious information clustering method is used to realize the multimedia big data network. Efficient retrieval of malicious information. Through the method of experimental argumentation analysis, the validity of the human-computer interaction retrieval method is determined. The results show that the method has a high recall rate of 28.31% compared with the traditional method, and the retrieval accuracy is extremely high. In this paper, the method of network malicious information retrieval is effective and suitable for popularization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xu, L., Weiqun, W., Zengguang, H., et al.: Human-machine interactive control method for rehabilitation robots. Chin. Sci. Inf. Sci. 25(1), 101–103 (2018)
Juanjuan, Q.: Simulation of implicit feedback information retrieval for users of big data network. Comput. Simul. 36(9), 430–433+468 (2019)
Wei, Z., Hongxu, H., Jing, W.: Application of Bayesian network for information retrieval. Inf. Sci. 36(6), 136–141 (2018)
Yao, Z., Bo, L., Xiansheng, H., et al.: Introduction to the special topic of multimedia data processing and analysis. J. Softw. 74(4), 59–63 (2018)
Qi, Z.: Research situation analysis of multimedia classrooms in china based on big Data. Econ. Res. Guide 26(18), 165–166 (2017)
Agosti, M., Crestani, F., Gradenigo, G.: Towards data modelling in information retrieval. Compr. Remote Sens. 15(6), 143–162 (2018)
Berit, E.A., Janne, B.C., Lars, T.: Hospital nurses’ information retrieval behaviours in relation to evidence based nursing: a literature review. Health Inf. Libr. J. 5(1), 3–23 (2018)
He, J., Liming, N., Zeyi, S., Zhilei, R., Weiqiang, K., Tao, Z., Xiapu, L.: ROSF: leveraging information retrieval and supervised learning for recommending code snippets. IEEE Trans. Serv. Comput. 12(1), 34–46 (2019)
Lu, Mengye, Liu, Shuai: Nucleosome positioning based on generalized relative entropy. Soft. Comput. 23(19), 9175–9188 (2018). https://doi.org/10.1007/s00500-018-3602-2
Jihong, L., Weiguang, Z.: Study on the innovation of network information retrieval for university library in big data era. Agric. Network Inf. 1(4), 30–32 (2017)
Liu, S., Liu, D., Srivastava, G., et al.: Overview and methods of correlation filter algorithms in object tracking. Complex Intell. Syst. (2020). https://doi.org/10.1007/s40747-020-00161-4
Li, J., Li, N., Afsari, K., et al.: Integration of Building Information Modeling and Web Service Application Programming Interface for assessing building surroundings in early design stages. Build. Environ. 153(1), 91–100 (2019)
Stanton, L.: Eighth Circuit Says VoIP Is Information Service, Preempts Minnesota PUC. Telecommun. Rep. 84(17), 1,35–36 (2018)
Tang, Z., Srivastava, G., Liu, S.: Swarm intelligence and ant colony optimization in accounting model choices. J. Intell. Fuzzy Syst. 38(2),1–9 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Gong, Jj., Xie, Wd., Wang, Jh. (2021). Efficient Retrieval Method of Malicious Information in Multimedia Big Data Network Based on Human-Computer Interaction. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-030-67871-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-67871-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67870-8
Online ISBN: 978-3-030-67871-5
eBook Packages: Computer ScienceComputer Science (R0)