Open Access
| Issue |
SHS Web Conf.
Volume 156, 2023
International Conference on Teaching and Learning – Digital Transformation of Education and Employability (ICTL 2022)
|
|
|---|---|---|
| Article Number | 04001 | |
| Number of page(s) | 4 | |
| Section | T&L Intelligence and Analytics | |
| DOI | https://doi.org/10.1051/shsconf/202315604001 | |
| Published online | 13 January 2023 | |
- Verhoef, C.P., Broekhuizen, T., Bart, Y., Bhattacharya, A., John., Fabian, N., Haenlein, M. (2021). Journal of Business Research. https://doi.org/10.1016/j.jbusres.2019.09.022 [Google Scholar]
- Vitenko, T., Shanaida, V., Droździel, P., Madlenak, R. (2018). Assessment of higher education in global environmnet. INTED2018 Proceedings, pp. 4040-4045 [CrossRef] [Google Scholar]
- Awad, M., Khanna, R. (2015). Machine Learning. In: Efficient Learning Machines. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_1 [Google Scholar]
- Sarker, I.H.(2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160. https://doi.org/10.1007/s42979-021-00592-x [CrossRef] [Google Scholar]
- Alloghani, M., Obe, D., Mustafina, J., Hussain, A., Aljaaf, A. (2020). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. Supervised and Unsupervised Learning in Datascience. Springer. https://doi.org/10.1007/978-3030-22475-2_1 [Google Scholar]
- Estrada-Real, A.C., Cantu-Ortiz, F.J.(2022). A data analytics approach for university competitiveness: the QS world university rankings. Int J Interact Des Manuf 16, 871–891. https://doi.org/10.1007/s12008-022-00966-2 [CrossRef] [Google Scholar]
- Kumar, S., Chong, I. (2018). Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States. Int J Environ Res Public Health. 15(12):2907. doi: 10.3390/ijerph15122907. PMID: 30572595; PMCID: PMC6313491. [CrossRef] [Google Scholar]
- Velthoen, V., Dombry, C., Cai, J., Engelke, S., (2021). Gradient boosting for extreme quantile regression. arXiv. https://doi.org/10.48550/arXiv.2103.00808 [Google Scholar]
- Sigrist, F. (2021). Gaussian Process Boosting. arXiv. https://doi.org/10.48550/arXiv.2004.02653 [Google Scholar]
- Alexei, B. (2018). Evaluating Performance of Regression Machine Learning Models Using Multiple Error Metrics in Azure Machine Learning Studio. http://dx.doi.org/10.2139/ssrn.3177507 [Google Scholar]
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