ARCHIVES

1. Guan, R., Zhang, H., Liang, Y., Giunchiglia, F., Huang, L. and Feng, X., 2020. Deep feature-based text clustering and its explanation.
IEEE Transactions on Knowledge and Data Engineering, 34(8), pp.3669-3680.
2. Bouveyron, C., Celeux, G., Murphy, T.B. and Raftery, A.E., 2019. Model-based clustering and classification for data science: with
applications in R (Vol. 50). Cambridge University Press.
3. Zong, C., Xia, R. and Zhang, J., 2021. Text data mining (Vol. 711, p. 712). Singapore: Springer.
4. Kumar, S., & Singh, M. (2020). A Survey on Fake News Detection using Machine Learning Techniques. International Journal of
Advanced Research in Computer Science and Software Engineering, 10(7), 1-5.
5. scikit-learn: Machine Learning in Python – https://scikit-learn.org/
6. Kaggle – https://www.kaggle.com/
7. True.csv and fake.csv : https://www.kaggle.com/datasets/emineyetm/fake-news-detection-datasets
8. Pandas Documentation – https://pandas.pydata.org/docs/
9. I. Ahmad, M. Yousaf, S. Yousaf, et al., “Fake news detection using machine learning ensemble methods,” Complexity, pp. 1-11, 2020
10. W. He, Y. He, B. Li, et al., “A naive-Bayes-based fault diagnosis approach for analog circuit by using image-oriented feature extraction
and selection technique,” IEEE Access, vol. 8, pp. 5065- 5079, 2020.
11. Q. Xue, Y. Zhu, and J. Wang, “Joint distribution estimation and naïve bayes classification under local differential privacy,” IEEE
Transactions on Emerging Topics in Computing, vol. 9, pp. 2053- 2063, 2021.
12. H. A. Maddah, “Decision trees based performance analysis for influence of sensitizers characteristics in dye-sensitized solar cells,”
Journal of Advances in Information Technology, vol. 13, pp. 271- 276, 2022.
13. I. D. Mienye, Y. Sun, and Z. Wang, “Prediction performance of improved decision tree-based algorithms: A review,” Procedia
Manufacturing, vol. 35, pp. 698-703, 2019.
14. J. A. C. Moreano and N. B. L. S. Palomino, “Global facial recognition using gabor wavelet, support vector machines and 3D face
models,” Journal of Advances in Information Technology, vol. 11, pp. 143-148, 2020.
15. A. B. Gumelar, A. Yogatama, D. P. Adi, et al., “Forward feature selection for toxic speech classification using support vector machine
and random forest,” International Journal of Artificial Intelligence, vol. 11, pp. 717-726, 2022.
16. J. Cervantes, F. García-Lamont, L. Rodríguez, et al., “A comprehensive survey on support vector machine classification: Applications,
challenges and trends,” Neurocomputing, vol. 408, pp. 189-215, 2020.
17. I. Benchaji, S. Douzi, and B. E. Ouahidi, “Credit card fraud detection model based on LSTM recurrent neural networks,” Journal of
Advances in Information Technology, vol. 12, pp. 113- 118, 2021.
18. N. Yadav and A. K. Singh, “Bi-directional encoder representation of transformer model for sequential music recommender system,”
in Proc. Forum for Information Retrieval Evaluation, 2020, pp. 49- 53.
19. Brown, T., et al. (2020). Language Models are Few-Shot Learners.
20. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning.