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Original Article
Empathy Chat – AI Emotional Support & Guidance Chatbot
G. Lavanya1
Pradeep P2
Shylash K3
Sophia Infant S4
Sreeja J5
1 Assistant Professor, Department of Information Technology, Rathinam Technical Campus, Coimbatore, Tamilnadu, India. 2 3 4 5 Department of Information Technology, Rathinam Technical Campus, Coimbatore, Tamilnadu, India.
Published Online: March-April 2026
Pages: 62-65
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260602009
References
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed., Pearson Education, 2021.
2. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
3. D. Dua and C. Graff, “UCI Machine Learning Repository,” University of California, Irvine, School of Information and Computer Sciences, 2019.
4. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794.
5. L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
6. P. Domingos and M. Pazzani, “On the Optimality of the Simple Bayesian Classifier under Zero-One Loss,” Machine Learning, vol. 29, no. 2–3, pp. 103–130, 1997.
7. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011.
8. World Health Organization, “Global Strategy on Digital Health 2020–2025,” WHO Press, Geneva, 2021.
9. E. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Basic Books, 2019.
10. R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep Learning for Healthcare: Review, Opportunities and Challenges,” Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236–1246, 2018.
11. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
12. M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” 2016.
13. M. Grinberg, Flask Web Development: Developing Web Applications with Python, 2nd ed., O’Reilly Media, 2018.
2. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
3. D. Dua and C. Graff, “UCI Machine Learning Repository,” University of California, Irvine, School of Information and Computer Sciences, 2019.
4. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794.
5. L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
6. P. Domingos and M. Pazzani, “On the Optimality of the Simple Bayesian Classifier under Zero-One Loss,” Machine Learning, vol. 29, no. 2–3, pp. 103–130, 1997.
7. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011.
8. World Health Organization, “Global Strategy on Digital Health 2020–2025,” WHO Press, Geneva, 2021.
9. E. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Basic Books, 2019.
10. R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep Learning for Healthcare: Review, Opportunities and Challenges,” Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236–1246, 2018.
11. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
12. M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” 2016.
13. M. Grinberg, Flask Web Development: Developing Web Applications with Python, 2nd ed., O’Reilly Media, 2018.
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