Advancing Machine Learning Algorithms for Predictive Analytics in Big Data Using Cloud Infrastructure
Keywords:
Predictive analytics, machine learning, big data, cloud infrastructure, scalability, algorithm optimizationAbstract
The rapid growth of big data necessitates robust frameworks for predictive analytics, with machine learning (ML) emerging as a transformative solution. Leveraging cloud infrastructure further enhances scalability, computational power, and efficiency in handling voluminous datasets. This paper explores advancements in ML algorithms optimized for predictive analytics in big data contexts, emphasizing cloud-based implementation. A review of existing literature highlights trends, challenges, and future directions, supported by illustrative data and analyses.
References
Zhang, Y., and Li, H. "Ensemble Methods in Healthcare Analytics." Journal of Big Data Applications, vol. 14, no. 3, 2022, pp. 25-39.
Tan, R., and Zhao, M. "Overcoming Scalability Challenges in Big Data ML." IEEE Transactions on Cloud Computing, vol. 12, no. 2, 2021, pp. 123-134.
Dhanekulla, P. (2024). The role of middleware in integrating legacy systems with modern policy administration. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 10(6). https://doi.org/10.32628/CSEIT24106180
Liu, K., and Chen, J. "Distributed Frameworks for Cloud-Based ML." Cloud Computing Journal, vol. 10, no. 4, 2021, pp. 56-78.
Dhanekulla, P. (2024). Modernizing legacy systems in insurance: Strategies for seamless integration of cloud-based policy administration solutions. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 1484–1498.
Kumar, S., and Mehta, A. "AutoML: Transforming Predictive Analytics." Advances in Machine Learning, vol. 19, no. 1, 2022, pp. 87-104.
Smith, J., and Taylor, P. "Scaling Machine Learning Algorithms for Big Data Applications." Journal of Data Science and Engineering, vol. 17, no. 5, 2023, pp. 234-248.
Dhanekulla, P. (2023). Intelligent claims management in insurance: The role of AI in modern policy systems. International Journal of Application or Innovation in Engineering and Management, 12(1), Article No-1, 17–2.
Vinay, S. B. (2024). Automated data transformation processes for improved efficiency and accuracy in complex ETL workflows. International Journal of Data Engineering Research and Development (IJDERD), 1(2), 1–11.
Brown, E., and Davis, K. "The Role of Cloud Computing in Modern Machine Learning." IEEE Cloud Computing Magazine, vol. 9, no. 3, 2022, pp. 15-27.
Williams, R., and Patel, A. "Distributed Machine Learning Models in Cloud-Based Systems." Proceedings of the International Conference on Big Data, vol. 8, no. 1, 2021, pp. 78-92.
Choi, H., and Kim, S. "Deep Learning on Cloud Platforms: Challenges and Opportunities." Advances in Artificial Intelligence, vol. 23, no. 2, 2022, pp. 102-117.
Anderson, G., and Rodriguez, C. "Comparative Analysis of Machine Learning Frameworks in Cloud Environments." Journal of Cloud Technology, vol. 11, no. 4, 2021, pp. 192-207.
Vinay, S. B. (2024). A comprehensive analysis of artificial intelligence applications in legal research and drafting. International Journal of Artificial Intelligence in Law (IJAIL), 2(1), 1–7.
Dhanekulla, P. (2024). Blockchain and distributed ledger technology in modernizing insurance systems: Enhancing transparency and reducing fraud. International Journal of Computer Engineering and Technology (IJCET), 15(6), 275–290.
Lee, Y., and Zhang, T. "Optimizing Predictive Analytics Using AutoML on Cloud." International Journal of Big Data Analytics, vol. 6, no. 2, 2023, pp. 45-60.
Vasudevan, K. (2024). The influence of AI-produced content on improving accessibility in consumer electronics. Indian Journal of Artificial Intelligence and Machine Learning (INDJAIML), 2(1), 1–11.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nivedhaa. N, (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.