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Research on Application of Big Data Combined with Probability Statistics in Training Applied Talents

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12240))

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Abstract

In order to improve the training effect of applied talents, quantitative evaluation and big data analysis are used to evaluate the training of applied talents. A quantitative analysis model of applied talents training based on big data and probability statistics is proposed. The statistical mathematical analysis model of applied talents training is constructed, and the significance of applied talents training is analyzed by using T statistical test analysis method, and the benefit distribution model of applied talents training is established. The cumulative average analysis method is used to evaluate the bilateral reliability of applied talents training, and the big data mining and feature extraction methods are used to analyze the characteristics of applied talents training. The big data robust mining model for the cultivation of applied talents is constructed, and the descriptive statistical analysis method of single variable is taken, the statistical analysis of probability theory and big data analysis method are used to realize the evaluation of benefit index for the cultivation of applied talents. The results of empirical analysis show that the model has good accuracy and high level of confidence in the quantitative evaluation of applied talents training.

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References

  1. Shi, E., Li, Q., Gu, D., Zhao, Z.: Weather radar echo extrapolation method based on convolutional neural networks. J. Comput. Appl. 38(3), 661–665 (2018)

    Google Scholar 

  2. Fletcher, T.D., Andrieu, H., Hamel, P.: Understanding, management and modelling of urban hydrology and its consequences for receiving waters, a state of the art. Adv. Water Resour. 51(1), 261–279 (2013)

    Article  Google Scholar 

  3. Ma, B., Xie, X.: PSHO-HF-PM: an efficient proactive spectrum handover mechanism in cognitive radio networks. Wirel. Pers. Commun. 79(3), 1–23 (2014)

    Article  Google Scholar 

  4. Ji, Y., Li, Y., Shi, C.: Aspect rating prediction based on heterogeneous network and topic model. J. Comput. Appl. 37(11), 3201–3206 (2017)

    Google Scholar 

  5. Xue, J., Ni, X.: On the reform of college english teaching under the trend of educational informatization. Integr. Inf. Technol. Teach. Pract. 45(12), 43–45 (2015)

    Google Scholar 

  6. Zheng, B., Gu, X.: Walk through the “cloud” end of practical education information–on the application of cloud platform in English teaching. In: Education and Teaching Forum, vol. 03, pp. 263–265 (2016)

    Google Scholar 

  7. Shen, W., Wynter, L.: A new one-level convex optimization approach for estimating origin–destination demand. Transp. Res. Part B: Methodol. 46(10), 1535–1555 (2012)

    Article  Google Scholar 

  8. Rao, C.S., Reddy, K.C.K., Rao, D.S.: Power control technique for efficient call admission control in advanced wireless networks. Int. J. Comput. Sci. Eng. 4(6), 962–973 (2012)

    Google Scholar 

  9. Shi, H.-Y., Wang, W.-L., Kwok, N.M., et al.: Game theory for wireless sensor networks: a survey. Sensors 12(7), 9055–9097 (2012)

    Article  Google Scholar 

  10. Zhang, G.-P., Liu, P., Ding, E.-J.: Energy efficient resource allocation in non-cooperative multi-cell OFDMA systems. J. Syst. Eng. Electron. 22(1), 175–182 (2011)

    Article  Google Scholar 

  11. Xiong, X., Yang, L., Ma, Y., Zhuang, Z.: Alerting algorithm of low-level wind shear based on fuzzy C-means. J. Comput. Appl. 38(3), 655–660 (2018)

    Google Scholar 

  12. Zheng, J.F., Zhang, J., Zhu, K.Y., et al.: Gust front statistical characteristics and automatic identification algorithm for CINRAD. Acta Meteorologica Sinica 28(4), 607–623 (2014). https://doi.org/10.1007/s13351-014-3240-2

    Article  Google Scholar 

  13. Hwang, Y., Yu, T.Y., Lakshmanan, V., et al.: Neuro-fuzzy gust front detection algorithm with S-band polarimetric radar. IEEE Trans. Geosci. Remote Sens. 55(3), 1618–1628 (2017)

    Article  Google Scholar 

  14. Sun, H., Zhang, H., Wu, J.: Correlated scale-free network with community: modeling and transportation dynamics. Nonlinear Dyn. 69(4), 2097–2104 (2012)

    Article  Google Scholar 

  15. Killip, R., Visan, M.: The defocusing energy-supercritical nonlinear wave equation in three space dimensions. Trans. Am. Math. Soc. 363(7), 3893–3934 (2011)

    Article  MathSciNet  Google Scholar 

  16. Sun, C.L., Qian, M.M.: The research on applied talenttraining mechanism of undergraduate based on the combination of professional programmatic accreditation and double certificates. In: International Computer Conference on Wavelet Active Media Technology and Information Processing, vol. 34, no. 8, 316–319 (2014)

    Google Scholar 

  17. Nuo, L.I.: The training scheme of applied talents on combination of enterprises and universities research in local universities. J. Jiaying Univ. 42(15), 321–327 (2011)

    Google Scholar 

  18. Yang, L.Y., Jiang, J.L.: Investigation and research on applied talents training of educational technology specialty in local undergraduate universities—with Jiaying University as example. J. Jiaying Univ. 41(9), 241–249 (2013)

    Google Scholar 

  19. Sun, L., Shen, Q., Zhao, L.: Construction of applied talents training mode for the statistics majors based on the Big Data technology. J. Jilin Inst. Chem. Technol. 34(6), 35–40 (2017)

    Google Scholar 

  20. Yu, Y.: Study on probability and mathematical statistics teaching in the training pattern of applied professionals. J. Langfang Teachers Univ. (Nat. Sci. Ed.) 16(4), 118–119 (2016)

    Google Scholar 

  21. Peng, T., Sun, L., Liu, C.: Research on applied IT talents cultivation based on the OBE model—big data approach. Softw. Eng. 20(8), 56–58 (2017)

    Google Scholar 

  22. Zhang, C., Yang, C., Wu, S., Zhang, X., Nie, W.: A straightforward direct traction boundary integral method for two-dimensional crack problems simulation of linear elastic materials. Comput. Mater. Continua 58(3), 761–775 (2019)

    Article  Google Scholar 

  23. Yuan, F., Zhang, Q., Xia, X.: Effect of reinforcement corrosion sediment distribution characteristics on concrete damage behavior. Comput. Mater. Continua 58(3), 777–793 (2019)

    Article  Google Scholar 

  24. Guo, F., et al.: Research on the law of garlic price based on big data. Comput. Mater. Continua 58(3), 795–808 (2019)

    Article  Google Scholar 

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Wu, L., Yang, J. (2020). Research on Application of Big Data Combined with Probability Statistics in Training Applied Talents. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_59

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  • DOI: https://doi.org/10.1007/978-3-030-57881-7_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57880-0

  • Online ISBN: 978-3-030-57881-7

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