ARCHIVES
Year 2026 · Volume 6 · Issue 5
Original Article
Learning from Sequences: A Review of Machine Learning and Deep Learning for Text, Speech, Behavioural, Financial and Genomic Data
Kavana K Kulkarani1
Kavya V2
1 2 Teaching Assistant, Department of Computer Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka, India.
Published Online: September-October 2026
Pages: 400-407
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605043
References
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9. B. Arivazhagan, S. Pandikumar, S. B. Sethupandian, and R. S. Subramanian, "Pattern discovery and analysis of customer buying behavior using association rules mining algorithm in e-commerce," in Proc. Int. Conf. Electr., Electron., Inf. Commun. Technol. (ICEEICT), Trichy, India, 2022, pp. 1–5, doi: 10.1109/ICEEICT53079.2022.9768473.
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15. V. Pandimurugan, D. Usha, M. N. Guptha, and M. S. Hema, "Random forest tree classification algorithm for predicating loan," Mater. Today: Proc., vol. 57, pp. 2216–2222, 2022.
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19. K. Huang, C. Xiao, L. M. Glass, and J. Sun, "MolTrans: Molecular interaction transformer for drug–target interaction prediction," Bioinformatics, vol. 37, no. 6, pp. 830–836, 2021, doi: 10.1093/bioinformatics/btaa880.
20. K. B. S. Prakash, M. Sudharsan, G. Meenakashi, and T. Margaret Mary, "Feature extraction of drug-target interactions using modified Transformer binding phase," in Proc. 2nd Int. Conf. Recent Advances in Information Technology for Sustainable Development (ICRAIS), 2024, pp. 250–255, doi: 10.1109/ICRAIS62903.2024.10811734.
21. J. Jumper et al., "Highly accurate protein structure prediction with AlphaFold," Nature, vol. 596, no. 7873, pp. 583–589, 2021, doi: 10.1038/s41586-021-03819-2.
2. G. Eraslan, Ž. Avsec, J. Gagneur, and F. J. Theis, "Deep learning: New computational modelling techniques for genomics," Nat. Rev. Genet., vol. 20, no. 7, pp. 389–403, 2019, doi: 10.1038/s41576-019-0122-6.
3. S. Pandikumar, B. Maram, T. Margaret Mary, and Nageshwari, "Personalized and context-aware text suggestion using deep reinforcement learning," in Proc. Int. Conf. Future and Advanced Computing Technologies (ICFACT), Krishnankoil, India, 2026, pp. 1–7, doi: 10.1109/ICFACT66887.2026.11518436.
4. S. Pandikumar, C. Menaka, T. Kiran, and T. J. P. Antony, "Evolution and analysis of modern plagiarism detection methods: A systematic review," J. Bus. Manag. Inf. Syst., Nov. 2024.
5. T. Foltýnek, N. Meuschke, and B. Gipp, "Academic plagiarism detection: A systematic literature review," ACM Comput. Surv., vol. 52, no. 6, Art. no. 112, 2019, doi: 10.1145/3345317.
6. S. Pandikumar, S. B. Sethupandian, M. S. Saravanan, S. N. Prasad, and M. Arun, "Deep learning based long short-term memory recurrent neural network for stock price movement prediction," Indian J. Sci. Technol., vol. 15, no. 11, pp. 474–480, 2022, doi: 10.17485/IJST/v15i11.27.
7. O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, "Financial time series forecasting with deep learning: A systematic literature review: 2005–2019," Appl. Soft Comput., vol. 90, p. 106181, 2020, doi: 10.1016/j.asoc.2020.106181.
8. M. Sudharsan, K. Maithili, T. Ravi, T. Margaret Mary, M. Rajesh Khanna, and P. Eswaran, "Deep learning and neural networks: Unlocking complex patterns in genomic medicine," in Pharmacogenomics Using Artificial Intelligence: Optimizing Drug Response through Personalized Genomic Analysis. Hoboken, NJ, USA: Wiley, 2026, pp. 75–99.
9. B. Arivazhagan, S. Pandikumar, S. B. Sethupandian, and R. S. Subramanian, "Pattern discovery and analysis of customer buying behavior using association rules mining algorithm in e-commerce," in Proc. Int. Conf. Electr., Electron., Inf. Commun. Technol. (ICEEICT), Trichy, India, 2022, pp. 1–5, doi: 10.1109/ICEEICT53079.2022.9768473.
10. M. J. Page et al., "The PRISMA 2020 statement: An updated guideline for reporting systematic reviews," BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
11. M. V. Reddy, T. Margaret Mary, and M. Hanumanthappa, "Phoneme-to-speech dictionary for Indian languages," in Proc. Int. Conf. Soft-Computing and Networks Security (ICSNS), 2015, doi: 10.1109/ICSNS.2015.7292388.
12. Y. Ren, C. Hu, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, "FastSpeech 2: Fast and high-quality end-to-end text to speech," in Proc. Int. Conf. Learn. Represent. (ICLR), 2021, arXiv:2006.04558.
13. S. Pandikumar, C. Menaka, and N. Sevugapandi, "Behavior prediction in social networks using feedforward neural network algorithm," J. Bus. Manag. Inf. Syst., Nov. 2024.
14. M. M. Afsar, T. Crump, and B. Far, "Reinforcement learning based recommender systems: A survey," ACM Comput. Surv., vol. 55, no. 7, Art. no. 145, 2022, doi: 10.1145/3543846.
15. V. Pandimurugan, D. Usha, M. N. Guptha, and M. S. Hema, "Random forest tree classification algorithm for predicating loan," Mater. Today: Proc., vol. 57, pp. 2216–2222, 2022.
16. X. Dastile, T. Celik, and M. Potsane, "Statistical and machine learning models in credit scoring: A systematic literature survey," Appl. Soft Comput., vol. 91, p. 106263, 2020, doi: 10.1016/j.asoc.2020.106263.
17. J. Vamathevan et al., "Applications of machine learning in drug discovery and development," Nat. Rev. Drug Discov., vol. 18, no. 6, pp. 463–477, 2019, doi: 10.1038/s41573-019-0024-5.
18. K. Prakash, P. K. Solleti, T. Hussain, C. M. Reddy, T. Margaret Mary, and P. Arumugam, "AI-driven drug discovery: Accelerating therapeutic innovation through genomics," in Pharmacogenomics Using Artificial Intelligence: Optimizing Drug Response through Personalized Genomic Analysis. Hoboken, NJ, USA: Wiley, 2026, pp. 101–123, doi: 10.1002/9781394404469.ch5.
19. K. Huang, C. Xiao, L. M. Glass, and J. Sun, "MolTrans: Molecular interaction transformer for drug–target interaction prediction," Bioinformatics, vol. 37, no. 6, pp. 830–836, 2021, doi: 10.1093/bioinformatics/btaa880.
20. K. B. S. Prakash, M. Sudharsan, G. Meenakashi, and T. Margaret Mary, "Feature extraction of drug-target interactions using modified Transformer binding phase," in Proc. 2nd Int. Conf. Recent Advances in Information Technology for Sustainable Development (ICRAIS), 2024, pp. 250–255, doi: 10.1109/ICRAIS62903.2024.10811734.
21. J. Jumper et al., "Highly accurate protein structure prediction with AlphaFold," Nature, vol. 596, no. 7873, pp. 583–589, 2021, doi: 10.1038/s41586-021-03819-2.
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