Open Access
E3S Web Conf.
Volume 202, 2020
The 5th International Conference on Energy, Environmental and Information System (ICENIS 2020)
Article Number 15012
Number of page(s) 8
Section Smart Information System
Published online 10 November 2020
  1. K. Johnson and M. D. Hannon, “Measuring the Relationship Between Parent, Teacher, and Student Problem Behavior Reports and Academic Achievement: Implications for School Counselors,” Prof. Sch. Couns., vol. 18, no. 1, p. 2156759X0001800, Sep. (2014). [CrossRef] [Google Scholar]
  2. J. D. McLeod, R. Uemura, and S. Rohrman, “Adolescent Mental Health, Behavior Problems, and Academic Achievement,” J. Health Soc. Behav., vol. 53, no. 4, pp. 482–497, Dec. (2012). [CrossRef] [PubMed] [Google Scholar]
  3. O. E. El-Shenawy and A.-M. Shehata, “Applying Problem Behavior Theory in a Developing Arabic Country,” SAGE Open, vol. 4, no. 1, p. 215824401452181, Jan. (2014). [Google Scholar]
  4. A. K. Sanford and R. H. Horner, “Effects of Matching Instruction Difficulty to Reading Level for Students With Escape-Maintained Problem Behavior,” J. Posit. Behav. Interv., vol. 15, no. 2, pp. 79–89, Apr. (2013). [Google Scholar]
  5. K. J. Mullinix, T. J. Leeper, J. N. Druckman, and J. Freese, “The generalizability of survey experiments,” (2016). [Google Scholar]
  6. H. Alshenqeeti, “Interviewing as a Data Collection Method: A Critical Review,” English Linguist. Res., vol. 3, no. 1, Mar. (2014). [CrossRef] [Google Scholar]
  7. I. Krumpal, “Determinants of social desirability bias in sensitive surveys: a literature review,” Qual. Quant., vol. 47, no. 4, pp. 2025–2047, Jun. (2013). [Google Scholar]
  8. B. Dash, “Methods of Data Collection,” in Essentials of Nursing Research and Biostatistics, vol. 14, no. 2, Jaypee Brothers Medical Publishers (P) Ltd., 2017, pp. 175– 175. [CrossRef] [Google Scholar]
  9. F. Thabtah and D. Peebles, “A new machine learning model based on induction of rules for autism detection,” Health Informatics J., vol. I–23, p. 146045821882471, Jan. (2019). [Google Scholar]
  10. W. Bleidorn and C. J. Hopwood, “Using Machine Learning to Advance Personality Assessment and Theory,” Personal. Soc. Psychol. Rev., vol. 23, no. 2, pp. 190–203, May (2019). [CrossRef] [Google Scholar]
  11. A. Awaysheh, J. Wilcke, F. Elvinger, L. Rees, W. Fan, and K. L. Zimmerman, “Review of Medical Decision Support and Machine-Learning Methods,” Vet. Pathol., vol. 56, no. 4, pp. 512–525, Jul. (2019). [Google Scholar]
  12. S. Klassen, J. Weed, and D. Evans, “Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval angkor, Cambodia,” PLoS One, vol. 13, no. 11, pp. 1–17, (2018). [CrossRef] [PubMed] [Google Scholar]
  13. N. R. Ravishankar and M. V. Vijayakumar, “Reinforcement Learning Algorithms: Survey and Classification,” Indian J. Sci. Technol., vol. 10, no. 1, pp. 1–8, (2017). [Google Scholar]
  14. E. Figueiredo, G. Park, C. R. Farrar, K. Worden, and J. Figueiras, “Machine learning algorithms for damage detection under operational and environmental variability,” Struct. Heal. Monit. An Int. J., vol. 10, no. 6, pp. 559–572, Nov. (2011). [CrossRef] [Google Scholar]
  15. K. M. H. Swhli, S. Jovic, N. Arsic, and P. Spalevic, “Detection and evaluation of heating load of building by machine learning,” Sens. Rev., vol. 38, no. 1, pp. 99–101, Jan. (2018). [CrossRef] [Google Scholar]
  16. Y. W. Oh and C. H. Park, “Machine Cleaning of Online Opinion Spam: Developing a Machine-Learning Algorithm for Detecting Deceptive Comments,” Am. Behav. Sci., p. 000276421987823, Oct. (2019). [Google Scholar]
  17. C. Hu and R. Albertani, “Machine learning applied to wind turbine blades impact detection,” Wind Eng., p. 0309524X1984985, May (2019). [Google Scholar]
  18. I. Qabajeh, F. Thabtah, and F. Chiclana, “A dynamic rule-induction method for classification in data mining,” J. Manag. Anal., vol. 2, no. 3, pp. 233–253, Jul. (2015). [Google Scholar]
  19. M. Berry and M. Browne, Lecture notes in data mining. (2006). [Google Scholar]
  20. J. Brownlee, Machine Learning Mastery With Weka, V1.1.(2016). [Google Scholar]

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