Mobile QR Code QR CODE

2024

Acceptance Ratio

21%

REFERENCES

1 
T. R. Kumar, T. Vamsidhar, B. Harika, T. M. Kumar, and R. Nissy, ``Students performance prediction using data mining techniques,'' Proc. of 2019 International Conference on Intelligent Sustainable Systems (ICISS), pp. 407-411, 2019.DOI
2 
H. E. Abdelkader, A. G. Gad, A. A. Abohany, and S. E. Sorour, ``An efficient data mining technique for assessing satisfaction level with online learning for higher education students during the COVID-19,'' IEEE Access, vol. 10, pp. 6286-6303, 2022.DOI
3 
R. Katarya, J. Gaba, A. Garg, and V. Verma, ``A review on machine learning based student’s academic performance prediction systems,'' Proc. of 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 254-259, 2021.DOI
4 
S. Sood, and M. Saini, ``Hybridization of cluster-based LDA and ANN for student performance prediction and comments evaluation,'' Education and Information Technologies, vol. 26, no. 3, pp. 2863-2878, 2021.DOI
5 
S. Poudyal, M. J. Mohammadi-Aragh, and J. E. Ball, ``Prediction of student academic performance using a hybrid 2D CNN model,'' Electronics, vol. 11, no. 7, pp. 1-21, 2022.DOI
6 
F. R. Trindade and D. J. Ferreira, ``Student performance prediction based on a framework of teacher's features,'' International Journal for Innovation Education and Research, vol. 9, no. 2, pp. 178-196, 2021.DOI
7 
A. J. Baruah and S. Baruah, ``Data augmentation and deep neuro-fuzzy network for student performance prediction with MapReduce framework,'' International Journal of Automation and Computing, vol. 18, no. 6, pp. 981-992, 2021.DOI
8 
S. Ranjeeth, T. P. Latchoumi, and P. V. Paul, ``Optimal stochastic gradient descent with multilayer perceptron based student's academic performance prediction model,'' Recent Advances in Computer Science and Communications, vol. 12, no. 1, pp. 1-14, 2020.DOI
9 
L. Barik, A. A. Alrababah, and Y. Al-Otaibi, ``Enhancing educational data mining based ICT competency among e-learning tutors using statistical classifier,'' International Journal of Advanced Computer Science and Applications, vol. 11, no. 3, pp. 561-568, 2020.DOI
10 
N. Khodeir, ``Student modeling using educational data mining techniques,'' Proc. of 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT), pp. 7-14, 2019.DOI
11 
S. Wan and Z. Niu, ``A hybrid e-learning recommendation approach based on learners' influence propagation,'' IEEE Transactions on Knowledge and Data Engineering, pp. 827-840, 2020.DOI
12 
C. Vieira, P. Parsons, and V. Byrd, ``Visual learning analytics of educational data: A systematic literature review and research agenda,'' Computers & Education, vol. 122, pp. 119-135, 2018.DOI
13 
D. Shah and T. Zaveri, ``Hyperspectral endmember extraction using Pearson's correlation coefficient,'' International Journal of Computational Science and Engineering, vol. 24, no. 1, pp. 1-9, 2021.DOI
14 
N. Gokilavani and B. Bharathi, ``An enhanced adaptive random sequence (EARS) based test case prioritization using K-mediods based fuzzy clustering,'' Proc. of 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 567-572, 2020.DOI
15 
D. Rains, H. Lievens, G. J. M. D. Lannoy, M. F. Mccabe, R. A. M. de Jeu, and D. G. Miralles, ``Sentinel-1 backscatter assimilation using support vector regression or the water cloud model at European soil moisture sites,'' IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.DOI
16 
T. V. Su and N. D. Hien, ``Strong Karush-Kuhn-Tucker optimality conditions for weak efficiency in constrained multiobjective programming problems in terms of mordukhovich subdifferentials,'' Optimization Letters, vol. 15, no. 4, pp. 1175-1194, 2021.DOI
17 
J. Liang, ``Problems and solutions of art professional service rural revitalization strategy based on random forest algorithm,'' Wireless Communications and Mobile Computing, vol. 2022, no. 1, pp. 1-11, 2022.DOI
18 
S. Da˘gıstanlı, S. Sönmez, M. Ünsel, E. Bozda˘g, A. Kocata¸s, M. Bo¸sat, E. Yurtseven, Z. Caliskan, and M. G. Gunver ``A novel survival algorithm in COVID-19 intensive care patients: The classification and regression tree (CRT) method,'' African Health Sciences, vol. 21, no. 3, pp. 1083-1092, 2021.DOI
19 
S. B. Lee, Y. J. Kim, S. Hwang, H. Son, S. K. Lee, Y. I. Park, and Y. G. Kim, ``Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals,'' Parkinsonism & Related Disorders, vol. 95, pp. 77-85, 2022.DOI
20 
Y. Wu, Q. Zhang, Y. Hu, K. Sun-Woo, X. Zhang, H. Zhu, L. Jie, and S. Li, ``Novel binary logistic regression model based on feature transformation of XGBoost for type 2 diabetes mellitus prediction in healthcare systems,'' Future Generation Computer Systems, vol. 129, pp. 1-12, 2022.DOI
21 
F. A. Maruf, R. Pratama, and G. Song, ``DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost,'' Journal of Bioinformatics and Computational Biology, vol. 19, no. 6, pp. 1-16, 2021.DOI
22 
L. Yao, Z. Fang, Y. Xiao, J. Hou, and Z. Fu, ``An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine,'' Energy, vol. 214, 118866, 2021.DOI