Establishment of data mining-based public education administrative work automation system and student activity analysis
Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v6i3.996
Abstract
Keywords
data mining; text mining; self-directed learning; administrative work system automation
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2. Pyle D. Data Preparation for Data Mining. Morgan Kaufmann; 1999.
3. Jin Y. Development of word cloud generator software based on python. Procedia Engineering 2017; 174: 788–792. doi: 10.1016/j.proeng.2017.01.223
4. Hiemstra R. Self-Directed Learning. IACE Hall of Fame Repository; 1994.
5. Kowsari K, Jafari Meimandi K, Heidarysafa M, et al. Text classification algorithms: A survey. Information 2019; 10(4): 150. doi: 10.3390/info10040150
6. Minaee S, Kalchbrenner N, Cambria E, et al. Deep learning based text classification: A comprehensive review. arXiv 2020; arXiv:2004.03705. doi: 10.48550/arXiv.2004.03705
7. Mirończuk MM, Protasiewicz J. A recent overview of the state-of-the-art elements of text classification. Expert Systems with Applications 2018; 106: 36–54. doi: 10.1016/j.eswa.2018.03.058
8. Althobaiti MJ. BERT-based approach to Arabic hate speech and offensive language detection in Twitter: Exploiting emojis and sentiment analysis. International Journal of Advanced Computer Science and Applications 2022; 13(5): 972–980. doi: 10.14569/IJACSA.2022.01305109
9. Xu B, Guo X, Ye Y, Cheng J. An improved random forest classifier for text categorization. Journal of Computers 2012; 7(12): 2913–2920. doi: 10.4304/jcp.7.12.2913-2920
10. Virupakshappa R, Patil N. An enhanced segmentation technique and improved support vector machine classifier for facial image recognition. International Journal of Intelligent Computing and Cybernetics 2022; 15(2): 302–317. doi: 10.1108/IJICC-08-2021-0172
11. Rangayya, Virupakshappa, Patil N. Improved face recognition method using SVM-MRF with KTBD based KCM segmentation approach. International Journal of System Assurance Engineering and Management 2022; 1–12. doi: 10.1007/s13198-021-01483-3
12. Bühlmann P. Bagging, boosting and ensemble methods. In: Gentle JE, Härdle WK, Mori Y (editors). Handbook of Computational Statistics. Springer Berlin, Heidelberg; 2012. pp. 985–1022.
13. Vijayarani S, Ilamathi J, Nithya. Preprocessing techniques for text mining—An overview. International Journal of Computer Science & Communication Networks 2015; 5(1): 7–16.
14. Denny MJ, Spirling A. Text preprocessing for unsupervised learning: Why it matters, when it misleads, and what to do about it. Political Analysis 2018; 26(2): 168–189. doi: 10.1017/pan.2017.44
15. Fedus W, Goodfellow I, Dai AM. Maskgan: Better text generation via filling in the _. arXiv 2018; arXiv:1801.07736. doi: 10.48550/arXiv.1801.07736
16. Adeli H. Neural networks in civil engineering: 1989–2000. Computer-Aided Civil and Infrastructure Engineering 2002; 16(2): 126–142. doi: 10.1111/0885-9507.00219
17. Yu F, Xu X. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Applied Energy 2014; 134: 102–113. doi: 10.1016/j.apenergy.2014.07.104
18. Abiodun OI, Jantan A, Omolara AE, et al. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018; 4(11): e00938. doi: 10.1016/j.heliyon.2018.e00938
19. Sarkar D, Bali R, Ghosh T. Hands-on Transfer Learning with Python: Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras. Packt Publishing; 2018.
20. Bird S, Klein E, Loper E. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media; 2009.
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