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Construction and Application of Computer Network Experimental Teaching Platform Based on Big Data

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Application of Big Data, Blockchain, and Internet of Things for Education Informatization (BigIoT-EDU 2022)

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Abstract

With the development of computer science and the popularization of computer application, the social demand for computer talents is increasing day by day. This paper takes “wisdom” as the teaching material as the carrier, we should focus on serving teachers and students in higher vocational colleges. We should not only give play to the leading role of teachers in guiding, enlightening and monitoring the teaching process, but also fully reflect the initiative, creativity and enthusiasm of students as the main body of the learning process. Colleges and universities have successively set up computer related majors, and even training institutions have participated in the training of computer professionals. However, with the enrollment regulations in recent years With the continuous expansion of the model, the construction speed of some school laboratories is far from keeping up with the expansion of enrollment scale. Combined with the reality of our school, this paper analyzes that when the conditions of computer network laboratory can not meet the computer network experimental teaching temporarily, the computer network experimental teaching platform is constructed by using packet tracer, VMware, pdffactory and other virtualization software to ensure the computer network The experimental course went smoothly.

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References

  1. Da, W.J., Hu, J.: Gan Hong in big data environment: research and design of cookie based web platform identity authentication mechanism. Jiangxi Sci. (2), 21–26 (2018)

    Google Scholar 

  2. Li, C., Gan, H.: Cross platform application and development of Mu Dong based on MVP architecture. Sci. Technol. Plaza (5), 35–39 (2017)

    Google Scholar 

  3. Li, Y., Ma, S., Huang, R.: Learning analysis technology: service learning process design and optimization. Open Educ. Res. 18(5), 18–24 (2012)

    Google Scholar 

  4. Yu, X., Gu, X.: Learning activity flow: a behavior model for learning analysis. J. Distance Educ. (4), 20–28 (2013)

    Google Scholar 

  5. Liu, Y., Yu, Q., Li, S.: Research on intelligent detection method of multi-model fusion images based on deep learning. Electron. Meas. Technol. 44(20), 7 (2021)

    Google Scholar 

  6. Zhang, F., Zhang, C., Yang, H.: Color compensation method for projected images based on deep learning in combat assistance systems. J. Mil. Eng. 42(11), 6 (2021)

    Google Scholar 

  7. Lian, X., Liu, Z., Zhang, L.: An image recognition method for retinopathy based on deep learning. Comput. Appl. Softw. 38(1), 7 (2021)

    Google Scholar 

  8. Cao, Y., Liu, H., Jia, X., Li, X.: Review of image quality evaluation methods based on deep learning. Comput. Eng. Appl. 57(23), 10 (2021)

    Google Scholar 

  9. Wang, Y., Sun, W., Zhou, X.: Research on image recognition method of Chinese herbal medicine plants based on deep learning. Chin. Med. Inf. 37(6), 5 (2020)

    Google Scholar 

  10. Hao, S., Liu, Y.: Overview of image intrinsic attribute prediction methods based on deep learning. J. Graph. 42(3), 13 (2021)

    Google Scholar 

  11. Xu, Z., Chen, S.: Recognition and localization of key target points in weak texture images based on deep learning. Comput. Meas. Control 30(2), 7 (2022)

    Google Scholar 

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Correspondence to Zhangsheng Zhong .

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhong, Z. (2023). Construction and Application of Computer Network Experimental Teaching Platform Based on Big Data. In: Jan, M.A., Khan, F. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-031-23947-2_50

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  • DOI: https://doi.org/10.1007/978-3-031-23947-2_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23946-5

  • Online ISBN: 978-3-031-23947-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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