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A Decision-Making Method of Intelligent Distance Online Education Based on Cloud Computing

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Abstract

In order to improve the quality of online distance education and students' online learning, an intelligent online distance education decision-making method based on cloud computing is proposed. Using computing, we provide decision-making resources for online education decision-making through the resource layer; adopt the G1 deviation maximization method to calculate the combination weight, and determine the optimal decision-making scheme in combination with bipolar binary semantics and cloud model; moreover, we provide human–computer interaction windows to view the decision-making scheme at the application layer; and complete the optimal decision-making for intelligent online education by providing the management function of cloud computing services. Experimental results show that this method can effectively obtain a decision-making scheme for network education. After the application of this method, the students' learning ability and academic performance have been significantly improved.

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References

  1. Jing L, Bo Z, Tian Q, Xu W, Shi J (2020) Network education platform in flipped classroom based on improved cloud computing and support vector machine. J Intell Fuzzy Syst 39(99):1–11

    Google Scholar 

  2. Xiao C, Cai H, Su Y, Shen L (2020) Online teaching practices and strategies for inorganic chemistry using a combined platform based on dingtalk, learning@zju, and wechat. J Chem Educ 97(9):2940–2944

    Article  Google Scholar 

  3. Li X (2020) Design of university distance education system based on ASP technology under background of the Internet. Modern Electron Tech 43(23):178–181

    Google Scholar 

  4. Guo Z, Sun F (2019) Multi-attribute decision making method based on single-valued neutrosophic linguistic variables and prospect theory. J Intell Fuzzy Syst 37(4):5351–5362

    Article  Google Scholar 

  5. Cai CG, Xu XH, Wang P, Xue XJ (2019) Incomplete preference complex large group emergency decision making method based on trust degree of decision makers & combination weighting. Oper Res Manag Sci 28(05):17–25

    Google Scholar 

  6. Ma J (2019) Mixed-attribute generalized grey target decision-making method based on target of decision maker’s preference. Stat Decis 35(07):32–36

    Google Scholar 

  7. Kamili A, Fatima I, Hassan M, Parah SA, Vijaya Kumar V, Ambati LS (2020) Embedding information reversibly in medical images for e-health. J Intell Fuzzy Syst. Preprint, 1-10

  8. Liu S, He T, Dai J (2021) A survey of CRF algorithm based knowledge extraction of elementary mathematics in Chinese. Mob Netw Appl 26(5):1891–1903

    Article  Google Scholar 

  9. Asma B, Youcef D, Gautam S et al (2021) A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories. IEEE Trans Intell Transp Syst 22(7):4496–4506

    Article  Google Scholar 

  10. Liu S, Wang S, Liu X et al (2021) Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans Fuzzy Syst 29(1):90–102

    Article  Google Scholar 

  11. Jimmy MTW, Gautam S, Alireza J et al (2021) Security and privacy in shared HitLCPS using a GA-based multiple-threshold sanitization model. IEEE Trans Emerg Top Comput Intell online first,. https://doi.org/10.1109/TETCI.2020.3032701

    Article  Google Scholar 

  12. Wang Y (2020) An improved machine learning and artificial intelligence algorithm for classroom management of english distance education. J Intell Fuzzy Syst 40(29):1–12

    Google Scholar 

  13. Peng QJ (2019) Emergency decision support system demand data self-service mining simulation. Comput Simul 36(08):329–332

    Google Scholar 

  14. Youcef D, Asma B, Gautam S et al (2022) Fast and accurate deep learning framework for secure fault diagnosis in the industrial internet of things. IEEE Internet Things J, online first. https://doi.org/10.1109/JIOT.2021.3092275

    Article  Google Scholar 

  15. Liu S, Liu X, Wang S et al (2021) Fuzzy-aided solution for out-of-view challenge in visual tracking under iot assisted complex environment. Neural Comput Appl 33(4):1055–1065

    Article  Google Scholar 

  16. Naeem M, Khan MA, Abdullah S, Qiyas M, Khan S (2021) Extended topsis method based on the entropy measure and probabilistic hesitant fuzzy information and their application in decision support system. J Intell Fuzzy Syst 40(1):1–12

    Article  Google Scholar 

  17. Shuai L, Shuai W, Xinyu L et al (2021) Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans Multimedia 23:2188–2198

    Article  Google Scholar 

  18. Ambati LS, El-Gayar O (2019) Human activity recognition: a comparison of machine learning approaches. J Midwest Assoc Inf Syst 2021(1):49

    Google Scholar 

  19. Qi M, Qin J, Yang Y, Wang Y, Luo J (2021) Semantics-aware spatial-temporal binaries for cross-modal video retrieval. IEEE Trans Image Process 30(9):2989–3004

    Article  MathSciNet  Google Scholar 

  20. Lin JC-W, Srivastava G, Zhang Y et al (2021) Privacy-preserving multiobjective sanitization model in 6G IoT environments. IEEE Internet Things J 8(7):5340–5349

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Basic Education Department, Henan Polytechnic, Zhengzhou, 450018, China

    Jun-yan Tong

  2. Department of Mathematics and Computer Science, Brandon University, Brandon, Canada

    Gautam Srivastava

  3. Research Centre for Interneural Computing, China Medical University, Taichung, Taiwan

    Gautam Srivastava

Authors
  1. Jun-yan Tong
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  2. Gautam Srivastava
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Corresponding author

Correspondence to Gautam Srivastava.

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The authors have no relevant financial or non-financial interests to disclose. Junyan Tong provided the algorithm and experimental results, wrote the manuscript, Gautam Srivastava revised the paper, supervised and analyzed the experiment. We also declare that data availability and ethics approval is not applicable in this paper.

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Tong, Jy., Srivastava, G. A Decision-Making Method of Intelligent Distance Online Education Based on Cloud Computing. Mobile Netw Appl 27, 1151–1161 (2022). https://doi.org/10.1007/s11036-022-01945-3

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  • DOI: https://doi.org/10.1007/s11036-022-01945-3

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