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Original Article
AI-Powered Retail: Revolutionizing Share Market Operations for Efficiency and Customer Experience
Dr.T. Elavarasi11
Anamika I.S2
Charudharshni K3
Indhu T4
1 Assistant Professor, Department of Computer Science and Applications, Jeppiaar College of Arts and Science, Chennai, Tamil Nadu, India. 2 3 4 Students, Department of Computer Science and Applications, Jeppiaar College of Arts and Science, Chennai, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 347-354
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502038
References
1. Agarwal, A. (2018, October 5). Linear Regression using Python. Medium. Retrieved from https://towardsdatascience.com/linear-regression - Using-python-b136c91bf0a2.
2. Arora, S., Hazan, E., & Kale, S. (2012, May 1). The Multiplicative Weights Update Method: A Meta Algorithm and its Applications. Theory of Computing, 8(1), 121–164. https://doi.org/10.4086/toc.2012.v008a006.
3. Barla, N. (2023, April 19). Overfitting vs under fitting in Machine Learning: Everything You Need to Know [web log]. Retrieved 2023, from https://neptune.ai/blog/overfitting-vs-underfitting-in-machine-Learning.
4. Bias-Variance Trade off – Machine Learning. GeeksforGeeks. (2020, June3). Retrieved from https://www.geeksforgeeks.org/ml-bias-variance- Trade-off/
5. Bishop, J. (2021, December 2). From GameStop to Tesla – how social Media is driving the stock market. Maddyness UK. Retrieved from https://www.maddyness.com/uk/2021/12/02/from-gamestop-to-tesla- how-social-media-is-driving-the-stock-market/
6. Chaddha, A., & Yadav, S. (2022, August 31). Examining the predictive Power of moving averages in the stock market. Journal of Student Research, 11(3). https://doi.org/10.47611/jsrhs.v11i3.3382.
7. Abbas, N., Cohen, C., Grolleman, D. J., & Mosk, B. (2024, October 15). Artificial Intelligence Can Make Markets More Efficient and More Volatile. IMF. https://www.imf.org/en/Blogs/Articles/2024/10/15/artificial-intelligence-can-make-markets-more-efficient-and-more-volatile
8. Abdul kareem, A. A., Fayed, Z. T., Rady, S., Amin El-Regaily, S., & Nema, B. M. (2023). Factors Influencing Investment Decisions in Financial Investment Companies. Systems, 11, Article No. 146. https://doi.org/10.3390/systems11030146
9. Adlakha, N., Ridhima, & Katal, A. (2021). Real Time Stock Market Analysis. In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp.1-5). IEEE. https://doi.org/10.1109/icscan53069.2021.9526506
10. Amplify ETFs (2024). Amplify ETFs—AIEQ. Amplify ETFs. https://amplifyetfs.com/aieq/
11. Au, T. C. (2018). Random Forests, Decision Trees, and Categorical Predictors: The “Absent Levels” Problem. Journal of Machine Learning Research, 19, 1-30.
12. Awan, M. J., Rahim, M. S. M., Nobanee, H., Munawar, A., Yasin, A., & Zain, A. M. (2021). Social Media and Stock Market Forecasting: A Big Data Perspective. Computers, Materials and Continua, 67, 2569-2583. https://doi.org/10.32604/cmc.2021.014253
2. Arora, S., Hazan, E., & Kale, S. (2012, May 1). The Multiplicative Weights Update Method: A Meta Algorithm and its Applications. Theory of Computing, 8(1), 121–164. https://doi.org/10.4086/toc.2012.v008a006.
3. Barla, N. (2023, April 19). Overfitting vs under fitting in Machine Learning: Everything You Need to Know [web log]. Retrieved 2023, from https://neptune.ai/blog/overfitting-vs-underfitting-in-machine-Learning.
4. Bias-Variance Trade off – Machine Learning. GeeksforGeeks. (2020, June3). Retrieved from https://www.geeksforgeeks.org/ml-bias-variance- Trade-off/
5. Bishop, J. (2021, December 2). From GameStop to Tesla – how social Media is driving the stock market. Maddyness UK. Retrieved from https://www.maddyness.com/uk/2021/12/02/from-gamestop-to-tesla- how-social-media-is-driving-the-stock-market/
6. Chaddha, A., & Yadav, S. (2022, August 31). Examining the predictive Power of moving averages in the stock market. Journal of Student Research, 11(3). https://doi.org/10.47611/jsrhs.v11i3.3382.
7. Abbas, N., Cohen, C., Grolleman, D. J., & Mosk, B. (2024, October 15). Artificial Intelligence Can Make Markets More Efficient and More Volatile. IMF. https://www.imf.org/en/Blogs/Articles/2024/10/15/artificial-intelligence-can-make-markets-more-efficient-and-more-volatile
8. Abdul kareem, A. A., Fayed, Z. T., Rady, S., Amin El-Regaily, S., & Nema, B. M. (2023). Factors Influencing Investment Decisions in Financial Investment Companies. Systems, 11, Article No. 146. https://doi.org/10.3390/systems11030146
9. Adlakha, N., Ridhima, & Katal, A. (2021). Real Time Stock Market Analysis. In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp.1-5). IEEE. https://doi.org/10.1109/icscan53069.2021.9526506
10. Amplify ETFs (2024). Amplify ETFs—AIEQ. Amplify ETFs. https://amplifyetfs.com/aieq/
11. Au, T. C. (2018). Random Forests, Decision Trees, and Categorical Predictors: The “Absent Levels” Problem. Journal of Machine Learning Research, 19, 1-30.
12. Awan, M. J., Rahim, M. S. M., Nobanee, H., Munawar, A., Yasin, A., & Zain, A. M. (2021). Social Media and Stock Market Forecasting: A Big Data Perspective. Computers, Materials and Continua, 67, 2569-2583. https://doi.org/10.32604/cmc.2021.014253
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