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Feature recognition of student using heuristic political effective evaluation reinforcement learning algorithm

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Abstract

The accomplishments of the students are reflected by ideological and political emotions within the school. A major factor is the risk factors in the lack of team cohesion in the ideological and political schools, the expansion of the environmental network, and the promotion of concurrent quality assurance. In this paper, the Heuristic Political Effective Evaluation Reinforcement Learning Algorithm (HPEERLA) has been proposed to enhance group coordination, environmental network extension, and double process improvement promotion in ideological and political education. Systematic optimization analysis is integrated with HPEERLA to increase university productivity, description of practice, evaluation and academic reports in ideological and political education. The newly proposed model combines learning, association, identity, self-adaptation, and data processing, thus addressing their respective limitations. The simulation analysis is performed based on sensitivity, performance, and efficiency proves the reliability of the proposed framework. The experimental analysis of HPEERLA for Students participation in the democratic ratio is 85.36%, Students' Social Work Activities ratio as 82.74%, Students Contribution to Media in the political ratio is 88.25%, Political Engagement ratio is 89.45%, Students Efficiency in Political Evaluation ratio is 94.25%.

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References

  1. Zhang, Z., Zhang, D., Qiu, R.C.: Deep reinforcement learning for power system applications: an overview. CSEE J. Power Energy Syst. 6(1), 213–225 (2019)

    Google Scholar 

  2. Elazab, S., Alazab, M.: The effectiveness of the flipped classroom in higher education. In: 2015 Fifth International Conference on e-Learning (econf), pp. 207–211. IEEE (2015)

  3. Yassine, S., Kadry, S., Sicilia, M.A.: Application of community detection algorithms on learning networks. The case of Khan academy repository. Comput. Appl. Eng. Educ. 29(2), 411–424 (2020)

    Article  Google Scholar 

  4. Bui, Q.T., Van Pham, M., Hang, N.T., Nguyen, Q.H., Linh, N.X., Hai, P.M., Tuan, T.A., Van Cu, P.: Hybrid model to optimize object-based land cover classification by meta-heuristic algorithm: an example for supporting urban management in Ha Noi, Viet Nam. Int. J. Digit. Earth 12(10), 1118–1132 (2019)

    Article  Google Scholar 

  5. Rafique, M. U., Mohammed, A. M., Li, S., Khan, A. T., Kadry, S.: Integrating open-source tools for embedded software teaching: a case study. Adv. Eng. Educ. (2019)

  6. Verma, C., Stoffová, V., Illés, Z., Tanwar, S., Kumar, N.: Machine learning-based student’s native place identification for real-time. IEEE Access 8, 130840–130854 (2020)

    Article  Google Scholar 

  7. Kurdi, B., Gershman, S.J., Banaji, M.R.: Model-free and model-based learning processes in the updating of explicit and implicit evaluations. Proc. Natl. Acad. Sci. 116(13), 6035–6044 (2019)

    Article  Google Scholar 

  8. Elhoseny, M., Metawa, N., Darwish, A., Hassanien, A.E.: Intelligent information system to ensure quality in higher education institutions, towards an automated e-university. Int. J. Comput. Intell. Stud. 6(2–3), 115–149 (2017)

    Google Scholar 

  9. Elhoseny, M., Metawa, N., Hassanien, A. E. An automated information system to ensure quality in higher education institutions. In 2016 12th International Computer Engineering Conference (ICENCO), pp. 196–201. IEEE (2016)

  10. Pinto, J.P., Murari, V.: Real time sentiment analysis of political twitter data using machine learning approach. Int. Res. J. Eng. Technol. 6(4), 4124–4129 (2019)

    Google Scholar 

  11. Xu, X., Chen, Y., Zhang, J., Chen, Y., Anandhan, P., Manickam, A.: A novel approach for scene classification from remote sensing images using deep learning methods. Eur. J. Remote Sens. 1–13 (2020)

  12. Senthilkumar, K., Easwaramoorthy, S.: A survey on cyber security awareness among college students in Tamil Nadu. In: IOP Conference Series Materials Science and Engineering, vol. 263. (2017)

  13. Wiedemann, G.: Proportional classification revisited: Automatic content analysis of political manifestos using active learning. Soc. Sci. Comput. Rev. 37(2), 135–159 (2019)

    Article  Google Scholar 

  14. Saravanan, V., Anpalagan, A., Poongodi, T., Khan, F. (eds.): Securing IoT and Big Data: Next Generation Intelligence. CRC Press, Florida (2020)

    Google Scholar 

  15. Saravanan,V., Alagan, A., Naik, K.: Computational biology as a compelling pedagogical tool in computer science education. J. Comput. Sci. 11(1)

  16. Xiao, Y., Xiao, L., Zhang, H., Yu, S., Poor H.V.: Privacy aware recommendation: reinforcement learning based user profile perturbation. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2019)

  17. Ji, F., Hsu, C.H., Montenegro-Marin, C.E.: Evaluating and recognizing stressful periods and events of urban migrant children from microblog. Curr. Psychol. 1–9 (2020)

  18. Osma, J.I.P., Suarez, J.A.G., Marin, C.E.M., Molano, J.I.R.: Metric LMS: educational evaluation platforms. In 2016 11th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6. IEEE (2016)

  19. Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., Tsaneva-Atanasova, K.: Artificial intelligence, bias and clinical safety. BMJ Qual. Saf. 28(3), 231–237 (2019)

    Article  Google Scholar 

  20. Joo, H., Ahmed, S.H., Lim, Y.: Traffic signal control for smart cities using reinforcement learning. Comput. Commun. 154, 324–330 (2020)

    Article  Google Scholar 

  21. Fujimoto, S., Conti, E., Ghavamzadeh, M., Pineau, J.: Benchmarking batch deep reinforcement learning algorithms. arXiv preprint http://arxiv.org/abs/1910.01708 (2019)

  22. Fujimoto, S., Meger, D., Precup, D.: Off-policy deep reinforcement learning without exploration. In: International conference on machine learning, pp. 2052–2062. PMLR (2019)

  23. Gu, B., Zhang, X., Lin, Z., Alazab, M.: Deep multi-agent reinforcement learning-based resource allocation for internet of controllable things. IEEE Internet Things J. (2020)

  24. Spano, S., Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Giardino, D., Matta, M., Nannarelli, A., Re, M.: An efficient hardware implementation of reinforcement learning: The q-learning algorithm. IEEE Access 20(7), 186340–186351 (2019)

    Article  Google Scholar 

  25. Zheng, L., Ratliff, L.: Constrained upper confidence reinforcement learning. In: Learning for Dynamics and Control, pp. 620–629. PMLR (2020)

  26. Rajkumar, K., Ramachandran, M., Al-Turjman, F., Patan, R.: A reinforcement learning optimization for future smart cities using software defined networking. Int. J. Mach. Learn. Cybern. 1–13

  27. Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., Levine, S.: Meta-world: a benchmark and evaluation for multi-task and meta reinforcement learning. In: Conference on Robot Learning, pp 1094–1100. PMLR (2020)

  28. Ziemes, J.F., Hahn-Laudenberg, K., Abs, H.J.: The impact of schooling on trust in political institutions–Differences arising from students’ immigration backgrounds. Learn. Cult. Soc. Interact. 26, 100429 (2020)

    Article  Google Scholar 

  29. Peng, Z., Hu, J., Shi, K., Luo, R., Huang, R., Ghosh, B.K., Huang, J.: A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning. Appl. Math. Comput. 369, 124821 (2020)

    MathSciNet  MATH  Google Scholar 

  30. Botero, C.M., Zielinski, S.: The implementation of a world-famous tourism ecolabel triggers political support for beach management. Tour. Manag. Perspect. 35, 100691 (2020)

    Google Scholar 

  31. Zembylas, M.: Hannah Arendt’s political thinking on emotions and education implications for democratic education. Discourse Stud. Cult. Politics Edu. 41(4), 501–515 (2020)

    Article  Google Scholar 

  32. Zhang, P., Li, H., Ha, Q.P., Yin, Z.Y., Chen, R.P.: Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses. Adv. Eng. Inf. 45, 101097 (2020)

    Article  Google Scholar 

  33. Swire-Thompson, B., Ecker, U.K., Lewandowsky, S., Berinsky, A.J.: They might be a liar but they’re my liar: source evaluation and the prevalence of misinformation. Political Psychol. 41(1), 21–34 (2020)

    Article  Google Scholar 

  34. Chávez, K., Mitchell, K.M.: Exploring bias in student evaluations Gender, race, and ethnicity. PS: Political Sci. Politics 53(2), 270–274 (2020)

    Google Scholar 

  35. Bogedain, A., Hamm, R.: Strengthening local economy–an example of higher education institutions’ engagement in “co-creation for sustainability.” REGION. 7(2), 9–27 (2020)

    Article  Google Scholar 

  36. Arzate Cruz, C., Igarashi, T. A survey on interactive reinforcement learning: design principles and open challenges. In: Proceedings of the 2020 ACM Designing Interactive Systems Conference , pp. 1195–1209. (2020)

  37. Tayal, A., Kose, U., Solanki, A., Nayyar, A., Saucedo, J.A.: Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic, data envelopment analysis and machine learning. Comput. Intell. 36(1), 172–202 (2020)

    Article  MathSciNet  Google Scholar 

  38. https://www.kaggle.com/yemregundogmus/political-opinion-with-machine-learning

  39. https://www.kaggle.com/c/covid19-global-forecasting-week-5/discussion/137468

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Authors and Affiliations

  1. Xingtai University, Xingtai, 054001, Hebei, China

    Rui Wu

  2. Hebei Institute of Communications, Shijiazhuang, 051430, Hebei, China

    Zeye Huang

  3. New Energy Geological Team of Hebei Coalfield Geological Bureau, Xingtai, 054001, Hebei, China

    Zongming Zhou

  4. Department of Computer Science & Engineering, PSNA College of Engineering & Technology, Dindigul, Tamilnadu, 624622, India

    R. Gayathri

  5. Department of Computer Science, IFET College of Engineering, Villupuram, Tamilnadu, 605108, India

    R. Premalatha

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  1. Rui Wu
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  2. Zeye Huang
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Correspondence to Rui Wu.

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Wu, R., Huang, Z., Zhou, Z. et al. Feature recognition of student using heuristic political effective evaluation reinforcement learning algorithm. Prog Artif Intell 12, 133–146 (2023). https://doi.org/10.1007/s13748-021-00255-1

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