Abstract
The traditional anomaly detection method of intelligent teaching system has some problems, such as poor accuracy and response efficiency. Therefore, this paper proposes a distributed anomaly detection method of intelligent teaching system based on cloud computing. Collect the abnormal data of distributed intelligent teaching system through cloud computing method, calculate the local reachable density according to Gaussian distribution function, build a data management model, and use distributed technology to locate and manage the abnormal area of teaching data, so as to achieve the goal of data detection and identification. The experimental results show that this method can effectively improve the recall rate of anomaly detection data in intelligent teaching system, and the response efficiency has been effectively improved.
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
References
Lei, H.: Exploration and practice of abnormal flow detection in intelligent campus construction based on data mining. Taiwan Strait Sci. Technol. Ind. 35(09), 55–57 (2022)
Zhe, C., Chong, W., Zhiqiu, H.: Program design course teaching system based on dynamic analysis. Comput. Syst. Appl. 29(10), 114–119 (2020)
Yangyang, F.: Design of malicious tamper detection system for teaching system based on web crawler. Digital Commun. World 04, 122–123 (2020)
Lin, P.H., Chen, S.Y.: Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access, 99, 1–8 (2020)
Jun, L., Liyan, Z., Xiaoyuan, L., et al.: Cyclic redundancy check method of serial communication data flow based on ZigBee. Comput. Simul. 38(1), 226–230 (2021)
Jin, R., Wei, B., Luo, Y., et al.: Blockchain-based data collection with efficient anomaly detection for estimating battery state-of-health. IEEE Sens. J. 99, 1 (2021)
Wójcik, K., Piekarczyk, M.: Machine learning methodology in a system applying the adaptive strategy for teaching human motions. Sensors 20(1), 314–326 (2020)
Xiaoming, L., Yi, Y., Yue, Z.: Simulation of large data multi-resolution acquisition method based on Java3D network. Comput. Simul. 37(2), 5–18 (2020)
Kumar, R., Patil, O., Karthik, N.S., et al.: A machine vision-based cyber-physical production system for energy efficiency and enhanced teaching-learning using a learning factory. Procedia CIRP 98(5), 424–429 (2021)
Ll, A., Yu, P.A., Lwb, C., et al.: Improving EGT sensing data anomaly detection of aircraft auxiliary power unit. Chin. J. Aeronaut. 33(2), 448–455 (2020)
Wu, L.: Student model construction of intelligent teaching system based on Bayesian network. Pers. Ubiquit. Comput. 24(3), 419–428 (2020)
Jiong, G., Qian, R., Jianjiang, H.: A summary of foreign research on the application of artificial intelligence in teaching. Audio Vis. Educ. Res. 41(2), 9–18 (2020)
Jian, M., Ligang, N., Xin, L.: Design of laboratory intelligent teaching system in key universities based on multimedia and network technology. Mod. Electron. Technol. 44(20), 6–16 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zheng, F., Ma, L., Yang, H., Liu, L. (2022). Research on Anomaly Detection of Distributed Intelligent Teaching System Based on Cloud Computing. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-031-21161-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-21161-4_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21160-7
Online ISBN: 978-3-031-21161-4
eBook Packages: Computer ScienceComputer Science (R0)