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Web Based Adaptive Integration Method of College Students’ Comprehensive Quality Evaluation Data

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Multimedia Technology and Enhanced Learning (ICMTEL 2023)

Abstract

In order to quantitatively analyze the comprehensive quality of college students, an adaptive integration method of college students’ comprehensive quality evaluation data based on Web is proposed. Build a Web network model to simulate the evaluation process of college students’ comprehensive quality and obtain quantitative evaluation data. After the evaluation data is cleaned and transformed, the random forest algorithm is used to determine the evaluation data type, and the adaptive integration result of the comprehensive quality evaluation data of college students is obtained. The experimental results show that the integrity coefficient of the integration results of the proposed method is about 0.047 higher than that of the comparison method, while the redundancy coefficient is reduced and the reliability coefficient is significantly improved. Applying it to the retrieval of evaluation data can effectively speed up the data retrieval.

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References

  1. Zavalina, O.L., Burke, M.: Assessing skill building in metadata instruction: quality evaluation of dublin core metadata records created by graduate students. J. Educ. Library Inform. Sci. 62(4), 423–442 (2021)

    Google Scholar 

  2. Vitali, E., Gadioli, D., Palermo, G., et al.: An efficient monte carlo-based probabilistic time-dependent routing calculation targeting a server-side car navigation system. 9(2), 1006–1019 (2021)

    Google Scholar 

  3. Durgadevi, P., Srinivasan, S.: Resource allocation in cloud computing using SFLA and cuckoo search hybridization. Int. J. Parall. Programm. 48(3), 549–565 (2018). https://doi.org/10.1007/s10766-018-0590-x

    Article  Google Scholar 

  4. Li, L., Dong, J., Zuo, D., et al.: SLA-aware and energy-efficient VM consolidation in cloud data centers using host state 3rd-order markov chain model. Chin. J. Electron. 29(6), 1207–1217 (2020)

    Article  Google Scholar 

  5. Guo, T., Shi, S., Yuan, J.: Missile data acquisition storage device design. J. Ordnance Equip. Eng. 41(6), 160–163 (2020)

    Google Scholar 

  6. Hao, G., Haibin, L., Jiao, F., et al.: Ship AIS data quality evaluation algorithm based on big data processing. Comput. Simul. 39(2), 298–303 (2022)

    Google Scholar 

  7. Rahul, K., Banyal, R.K.: Detection and correction of abnormal data with optimized dirty data: a new data cleaning model. Int. J. Inform. Technol. Decision Making 20(02), 809–841 (2021)

    Article  Google Scholar 

  8. Nagaveni, K.: Haar wavelet collocation method for solving the telegraph equation with variable coefficients. Int. J. Appl. Eng. Res. 15(3), 235–243 (2020)

    Google Scholar 

  9. Bedolla-Ibarra, M.G., del Carmen, M., Cabrera-Hernandez, M.A., Aceves-Fernández, S.-A.: Classification of attention levels using a random forest algorithm optimized with particle swarm optimization. Evol. Syst. 13(5), 687–702 (2022). https://doi.org/10.1007/s12530-022-09444-2

    Article  Google Scholar 

  10. Mekala, M.S., Jolfaei, A., Srivastava, G., et al.: Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems. IEEE Trans. Emerg. Topics Comput. Intell. 6(2), 245–254 (2020)

    Article  Google Scholar 

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Correspondence to Wenjing Liu .

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Liu, W., Yuan, H. (2024). Web Based Adaptive Integration Method of College Students’ Comprehensive Quality Evaluation Data. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-50571-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-50571-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50570-6

  • Online ISBN: 978-3-031-50571-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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