Machine Learning Enabled Enterprise Platforms for Cloud Modernization Predictive Analytics and Digital Trust

Authors

  • Alexandru Costan Alexandru Costan Author

DOI:

https://doi.org/10.15662/IJEETR.2022.0406016

Keywords:

Machine Learning, Enterprise Platforms, Cloud Modernization, Predictive Analytics, Digital Trust, Artificial Intelligence, Cloud Computing, Data Intelligence, Cybersecurity, Digital Transformation

Abstract

The rapid evolution of cloud computing, artificial intelligence, and data-driven technologies has accelerated enterprise modernization by enabling organizations to develop intelligent, scalable, and secure digital platforms. Traditional enterprise systems often experience limitations due to legacy infrastructure, inefficient decision-making processes, fragmented data management, and increasing cybersecurity challenges. This research proposes a Machine Learning Enabled Enterprise Platform framework that integrates cloud modernization, predictive analytics, and digital trust mechanisms to enhance enterprise agility, operational intelligence, and security resilience. The proposed framework utilizes machine learning algorithms to analyze enterprise data, identify patterns, predict future outcomes, and optimize business and IT operations. Cloud modernization capabilities enable scalable infrastructure management through cloud-native architectures, automated deployment, and intelligent resource optimization. Digital trust mechanisms strengthen enterprise security through identity management, data protection, transparency, and continuous risk assessment. The framework combines predictive analytics with secure cloud platforms to support proactive decision-making, automated operations, and reliable digital services. This research highlights the importance of integrating machine learning, cloud transformation, and trust-based governance to build future-ready enterprise ecosystems. The proposed approach enables organizations to improve operational efficiency, enhance customer confidence, reduce risks, and achieve sustainable digital transformation in highly dynamic business environments

References

1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.

2. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1, 7–18.

3. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. National Institute of Standards and Technology.

4. Buyya, R., Broberg, J., & Goscinski, A. (2011). Cloud Computing: Principles and Paradigms. Wiley.

5. Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75–86.

6. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19, 171–209.

7. Vankayala, S. C. (2019). Establishing Auditable and Privacy-Respectful Test Data Systems through Synthetic Data Engineering and Governance-Driven Anonymization. International Journal of Computer Technology and Electronics Communication, 2(6), 1809-1821.

8. Ajith, G., Sudarsaun, J., Arvind, S. D., & Sugumar, R. (2018). IoT based fire deduction and safety navigation system. Int. J. Innov. Res. Sci. Eng. Technol, 7(2).

9. Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130-134.

10. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.

11. Yamsani, N. (2018). Operationalizing regulatory governance through enterprise master data design: A practical examination of OFAC, KYC, and GDPR controls at Elavon. International Journal of Scientific Research & Engineering Trends, 4(6). https://doi.org/10.5281/zenodo.18196005

12. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.

13. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.

14. Mohammed, S. (2021). Hybrid cloud architecture strategy for global infrastructure operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6), 4078–4081.

15. Mathew, A. R. (2022). Threats and protection on E-sim: a prospective study. Novel Perspectives of Engineering Research, 8, 76-81.

16. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.

17. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105-5111.

18. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.

19. Dhinakaran, D., Prathap, P. J., Selvaraj, D., Kumar, D. A., & Murugeshwari, B. (2022). Mining privacy-preserving association rules based on parallel processing in cloud computing. International Journal of Engineering Trends and Technology, 70(3), 284-294.

20. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003

21. Polamreddy, V. R. (2021). Engineering Reversible Enterprise Data Migrations: A Phased Rollout Framework for Financially Critical Retail Platforms. International Journal of Computer Technology and Electronics Communication, 4(2), 3414-3427.

22. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.

23. Navandar, P. (2022). Adaptive SAP security control framework for ML driven anomaly detection, role based access hardening, and continuous compliance monitoring in SAP S/4HANA environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(3), 4939–4952. https://doi.org/10.15662/IJEETR.2022.0403005

24. Parasa, M. (2021). Encryption-aware data integrity and quality controls in SAP SuccessFactors integrations using machine learning and cryptographic hash chains for tamper detection. International Journal of Computer Technology and Electronics Communication, 4(6), 4304–4316. https://doi.org/10.15680/IJCTECE.2021.0406014

25. Kotla, Mutha Ravi Tej (2021). Machine learning-based predictive observability for enterprise integration platforms. Journal of Computer Engineering and Technology, 4(3), 1–24. https://doi.org/10.34218/JCET_04_03_001

26. Vimal, V. R., Anandan, P., & Kumaratharan, N. (2022). Heart Disease Diagnosis Using Electrocardiography (ECG) Signals. Intelligent Automation & Soft Computing, 32(1).

27. Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27.

28. Samiuddin Mohammed (2022). AI-driven IT operations and AIOps enablement across hybrid cloud platforms. International Journal of Innovative Research in Science, Engineering and Technology, 11(3), 2826–2836. https://doi.org/10.15680/IJIRSET.2022.1103134

29. Juvvadi, R. R. (2019). Smart contracts in supply chain finance: Automating accounts payable and the three-way match. Journal of Information Systems Engineering and Management, 4(1), 1–12.

30. Anand, L., & Syed Ibrahim, S. P. (2018). HANN: a hybrid model for liver syndrome classification by feature assortment optimization. Journal of medical systems, 42(11), 211.

31. Tamilvizhi, T., Surendran, R., Anbazhagan, K., & Rajkumar, K. (2022). Quantum Behaved Particle Swarm Optimization‐Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification. Mathematical Problems in Engineering, 2022(1), 3452413.

32. Adepu, G. (2021). Zero-Trust Digital Government Platforms: Secure Identity, API Governance, and Cloud-Native Service Architecture. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3089-3093.

33. Gopisetty, S. (2022). Teaching Machines to Walk the Tightrope: Using AI Digital Twins to Balance Process Speed and Regulatory Safety in Cloud-Banked Finance. Journal of Scientific and Engineering Research, 9(8), 183-226.

34. Manda, P. (2022). Implementing hybrid cloud architectures with Oracle and AWS: Lessons from mission-critical database migrations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7111–7122.

35. Vanitha, C., Sanmugam, A., Yogananth, A., Rajasekar, M., Kuppusamy, P. G., & Devasagayam, G. (2022). A facile synthesis of polyaniline-WO3 hybrid nanocomposite for enhanced dopamine detection. Materials Letters, 328, 133149.

36. Vayyasi, N. K. (2019). Reimagining financial compliance automation: Using Java microservices and generative AI on AWS Bedrock for regulatory intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 2(3), 1992–1210.

37. Li, X., Zhang, X., & Wang, Z. (2020). Machine learning based intelligent cloud resource management. IEEE Access, 8, 123000–123015.

38. Zhang, Y., Zheng, L., & Chen, X. (2021). AI-driven predictive analytics and digital trust management for enterprise cloud platforms. Future Internet, 13(4), 102.

39. Shwartz, S., & David, S. (2021). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

Downloads

Published

2022-10-08

How to Cite

Machine Learning Enabled Enterprise Platforms for Cloud Modernization Predictive Analytics and Digital Trust. (2022). International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5762-5769. https://doi.org/10.15662/IJEETR.2022.0406016