Cloud-Native AI Framework for Scalable Software Engineering: SAP-Integrated Optimization with Redundant Cyber Data Vaults and Adaptive Image Denoising
DOI:
https://doi.org/10.15662/IJRAI.2023.0603005Keywords:
Cloud-Native Computing, SAP AI for Business, Software Engineering, Machine Learning, Deep Learning, Cyber Data Vault, Image Denoising, Data Redundancy, System Resilience, AI Optimization, Intelligent Ecosystem, Fault ToleranceAbstract
The rapid evolution of artificial intelligence (AI) and cloud computing has redefined the paradigms of software engineering, emphasizing scalability, resilience, and automation. This paper proposes a Cloud-Native AI Framework that integrates SAP-based optimization, redundant cyber data vaults, and adaptive image denoising to enhance the reliability and intelligence of large-scale software ecosystems. The proposed model employs machine learning (ML) and deep learning (DL) algorithms within SAP AI for Business environments to automate code optimization, fault detection, and performance tuning of software modules in real time. The integration of redundant cyber data vaults ensures continuous data protection, recovery, and anomaly detection across distributed cloud infrastructures, mitigating the risks of cyber intrusions and data corruption. Additionally, the framework incorporates an adaptive image denoising algorithm—based on convolutional neural networks (CNNs) and hybrid wavelet transforms—to enhance visual data integrity and improve model accuracy in computer vision–dependent applications. Experimental validation demonstrates a 40% improvement in fault recovery speed, 25% enhancement in data availability, and significant noise reduction in image datasets, compared to conventional cloud-based systems. This research highlights the potential of AI-driven, SAP-integrated cloud frameworks to achieve self-healing, secure, and adaptive software ecosystems, suitable for deployment across healthcare, financial, and industrial domains.
References
1. Cvach, M. (2012). Monitor alarm fatigue: an integrative review. Biomedical Instrumentation & Technology, 46(4), 268–277.
2. Shaffi, S. M. (2023). The rise of data marketplaces: a unified platform for scalable data exchange and monetization. International Journal for Multidisciplinary Research, 5(3). https://doi.org/10.36948/ijfmr.2023.v05i03.45764
3. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
4. 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.
5. G Jaikrishna, Sugumar Rajendran, Cost-effective privacy preserving of intermediate data using group search optimisation algorithm, International Journal of Business Information Systems, Volume 35, Issue 2, September 2020, pp.132-151.
6. Azmi, S. K. (2021). Spin-Orbit Coupling in Hardware-Based Data Obfuscation for Tamper-Proof Cyber Data Vaults. Well Testing Journal, 30(1), 140-154.
7. CHAITANYA RAJA HAJARATH, K., & REDDY VUMMADI, J. . (2023). THE RISE OF INFLATION: STRATEGIC SUPPLY CHAIN COST OPTIMIZATION UNDER ECONOMIC UNCERTAINTY. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 14(2), 1115–1123. https://doi.org/10.61841/turcomat.v14i2.15247
8. Pimpale, S(2022). Safety-Oriented Redundancy Management for Power Converters in AUTOSAR-Based Embedded Systems. https://www.researchgate.net/profile/Siddhesh-Pimpale/publication/395955174_Safety-Oriented_Redundancy_Management_for_Power_Converters_in_AUTOSAR-Based_Embedded_Systems/links/68da980a220a341aa150904c/Safety-Oriented-Redundancy-Management-for-Power-Converters-in-AUTOSAR-Based-Embedded-Systems.pdf
9. Venkata Surendra Reddy Narapareddy, Suresh Kumar Yerramilli. (2022). SCALING THE SERVICE NOW CMDB FOR DISTRIBUTED INFRASTRUCTURES. International Journal of Engineering Technology Research & Management (IJETRM), 06(10), 101–113. https://doi.org/10.5281/zenodo.16845758
10. Drew, B. J., Harris, P., Zegre-Hemsey, J. K., Mammone, T., Schindler, D., Salas-Bonilla, M., ... & Bai, Y. (2014). Insights into the problem of alarm fatigue with physiologic monitor devices. Critical Care Medicine, 42(5), 1005–1011.
11. Sankar,, T., Venkata Ramana Reddy, B., & Balamuralikrishnan, A. (2023). AI-Optimized Hyperscale Data Centers: Meeting the Rising Demands of Generative AI Workloads. In International Journal of Trend in Scientific Research and Development (Vol. 7, Number 1, pp. 1504–1514). IJTSRD. https://doi.org/10.5281/zenodo.15762325
12. S. T. Gandhi, "Context Sensitive Image Denoising and Enhancement using U-Nets," Computer Science (MS), Computer Science (GCCIS), Rochester Institute of Technology, 2020. [Online]. Available: https://repository.rit.edu/theses/10588/
13. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
14. K. Anbazhagan, R. Sugumar (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud. Indian Journal of Science and Technology 9 (48):1-5.
15. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal of Computer Science Applications and Information Technology, 5(1), 1-8.
16. Srinivas Chippagiri, Preethi Ravula. (2021). Cloud-Native Development: Review of Best Practices and Frameworks for Scalable and Resilient Web Applications. International Journal of New Media Studies: International Peer Reviewed Scholarly Indexed Journal, 8(2), 13–21. Retrieved from https://ijnms.com/index.php/ijnms/article/view/294 - 2 cited
17. Chapman, W. W., Nadkarni, P. M., Hirschman, L., D’Avolio, L. W., Savova, G. K., & Uzuner, Ö. (2011). Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. Journal of the American Medical Informatics Association, 18(5), 540–543.
18. Roberts, K., Demner-Fushman, D., Ton-That, T., & Voorhees, E. (2020). Clinical Natural Language Processing challenges and opportunities. In Proceedings of the ClinicalNLP Workshop (ACL Anthology 2020).
19. Konda, S. K. (2022). ENGINEERING RESILIENT INFRASTRUCTURE FOR BUILDING MANAGEMENT SYSTEMS: NETWORK RE-ARCHITECTURE AND DATABASE UPGRADE AT NESTLÉ PHX. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6186-6201.
20. Batchu, K. C. (2022). Modern Data Warehousing in the Cloud: Evaluating Performance and Cost Trade-offs in Hybrid Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(6), 7343-7349.
21. Sangannagari, S. R. (2022). THE FUTURE OF AUTOMOTIVE INNOVATION: EXPLORING THE IN-VEHICLE SOFTWARE ECOSYSTEM AND DIGITAL VEHICLE PLATFORMS. International Journal of Research and Applied Innovations, 5(4), 7355-7367.
22. Shneiderman, B., Arif, A., &acles, P. (2021). Trust, explainability, and human-in-the-loop design for clinical AI systems. Journal of Medical Systems, 45, 23.
23. Jabed, M. M. I., Khawer, A. S., Ferdous, S., Niton, D. H., Gupta, A. B., & Hossain, M. S. (2023). Integrating Business Intelligence with AI-Driven Machine Learning for Next-Generation Intrusion Detection Systems. International Journal of Research and Applied Innovations, 6(6), 9834-9849.
24. Kuo, Y. F., & Davis, R. (2022). Forensic readiness in healthcare IT: metrics, automation and playbooks. International Journal of Digital Forensics & Incident Response, 38, 100487.
