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All issues Volume 149 (2022) SHS Web Conf., 149 (2022) 01048 References
Open Access
Issue
SHS Web Conf.
Volume 149, 2022
International Conference on Social Science 2022 “Integrating Social Science Innovations on Post Pandemic Through Society 5.0” (ICSS 2022)
Article Number 01048
Number of page(s) 5
Section Education and Digital Learning
DOI https://doi.org/10.1051/shsconf/202214901048
Published online 18 November 2022
  1. P. Blaga, “The importance of human resources in the continuous improvement of the production quality,” Procedia Manuf., vol. 46, pp. 287–293, (2020), doi: 10.1016/j.promfg.2020.03.042. [CrossRef] [Google Scholar]
  2. L. López-Torres and D. Prior, “Centralized allocation of human resources. An application to public schools,” Comput. Oper. Res., vol. 73, pp. 104–114, (2016), doi: 10.1016/j.cor.2016.04.001. [CrossRef] [Google Scholar]
  3. M. Lintang, N. Pandiangan, and R. Zubaedah, “Implementation of the Simple Multi Attribute Ranking Technique Method as a Model forDecision Making in Determining the Talents and Interests of Children in Continuing Education,” J. Phys. Conf. Ser., vol. 1899, no. 1, (2021), doi: 10.1088/1742-6596/1899/1/012075. [Google Scholar]
  4. R. Ferri-García, J. M. Fernández-Luna, C. Rodríguez-López, and P. Chillón, “Data mining techniques to analyze the factors influencing active commuting to school,” Int. J. Sustain. Transp., vol. 14, no. 4, pp. 308–323, (2020), doi: 10.1080/15568318.2018.1547465. [CrossRef] [Google Scholar]
  5. C. K. Lo, H. C. Chen, P. Y. Lee, M. C. Ku, L. Ogiela, and C. H. Chuang, “Smart dynamic resource allocation model for patient-driven mobile medical information system using C4.5 algorithm,” J. Electron. Sci. Technol., vol. 17, no. 3, pp. 231–241, (2019), doi: 10.11989/JEST.1674-862X.71018117. [Google Scholar]
  6. K. R. Pradeep and N. C. Naveen, “Lung Cancer Survivability Prediction based on Performance Using Classification Techniques of Support Vector Machines, C4.5 and Naive Bayes Algorithms for Healthcare Analytics,” Procedia Comput. Sci., vol. 132, pp. 412–420, (2018), doi: 10.1016/j.procs.2018.05.162. [CrossRef] [Google Scholar]
  7. W. Chao and W. Junzheng, “Cloud-service decision tree classification for education platform,” Cogn. Syst. Res., vol. 52, pp. 234–239, (2018), doi: 10.1016/j.cogsys.2018.06.021. [CrossRef] [Google Scholar]
  8. V. Matzavela and E. Alepis, “Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments,” Comput. Educ. Artif. Intell., vol. 2, p. 100035, (2021), doi: 10.1016/j.caeai.2021.100035. [CrossRef] [Google Scholar]
  9. X. Wang, C. Zhou, and X. Xu, “Application of C4.5 decision tree for scholarship evaluations,” Procedia Comput. Sci., vol. 151, no. (2018), pp. 179–184, 2019, doi: 10.1016/j.procs.2019.04.027. [CrossRef] [Google Scholar]
  10. N. Pandiangan, M. L. C. Buono, and S. H. D. Loppies, “Implementation of Decision Tree and Naïve Bayes Classification Method for Predicting Study Period,” J. Phys. Conf. Ser., vol. 1569, no. 2, (2020), doi: 10.1088/1742-6596/1569/2/022022. [CrossRef] [Google Scholar]
  11. H. Pallathadka, A. Wenda, E. Ramirez-Asís, M. Asís-López, J. Flores-Albornoz, and K. Phasinam, “Classification and prediction of student performance data using various machine learning algorithms,” Mater. Today Proc., no. xxxx, (2021), doi: 10.1016/j.matpr.2021.07.382. [Google Scholar]
  12. E. Teaching et al., “Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics : An Issue Brief 通过教育数据挖掘与学习分析提高教学与 学习质量 : 一份简短的发行报告,” ICTACT J. Soft Comput., vol. 05, no. 04, pp. 1035–1049, (2015). [CrossRef] [Google Scholar]
  13. Z. Qin and J. Lawry, “Decision tree learning with fuzzy labels,” Inf. Sci. (Ny)., vol. 172, no. 1–2, pp. 91–129, (2005), doi: 10.1016/j.ins.2004.12.005. [CrossRef] [Google Scholar]
  14. H. Hamsa, S. Indiradevi, and J. J. Kizhakkethottam, “Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm,” Procedia Technol., vol. 25, pp. 326–332, (2016), doi: 10.1016/j.protcy.2016.08.114. [CrossRef] [Google Scholar]
  15. J. F. Baldwin and D. W. Xie, “Simple fuzzy logic rules based on fuzzy decision tree for classification and prediction problem,” IFIP Adv. Inf. Commun. Technol., vol. 163, pp. 175–184, (2005), doi: 10.1007/0-387-23152-8_23. [Google Scholar]
  16. Ogunde and Ajibade, “A Data Mining System for Predicting University Students’ Graduation Grades Using ID3 Decision Tree Algorithm Ogunde A. O 1 . and Ajibade D. A 1 .,” Comput. Sci. Inf. Technol., vol. 2, no. 1, pp. 21–46, (2014). [Google Scholar]
  17. A. M. Hormann, “Programs for machine learning. Part II,” Inf. Control, vol. 7, no. 1, pp. 55–77, (1964), doi: 10.1016/S0019-9958(64)90259-1. [CrossRef] [Google Scholar]

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