Cloud-Enabled Intelligent Ecosystem for BMS: SAP AI Integration with Secure Data Layers, Digital Forensics, and Energy-Aware DC-DC Conversion
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The convergence of artificial intelligence (AI), secure data architectures, and power-efficient systems is transforming the next generation of Building Management Systems (BMS). This paper proposes a cloud-enabled intelligent ecosystem that integrates SAP AI for Business, secure data layers, and digital forensics with an energy-aware DC-DC conversion framework. The objective is to develop an adaptive, transparent, and self-optimizing architecture for critical infrastructure such as healthcare and industrial facilities. The proposed BMS framework utilizes machine learning and deep learning algorithms within the SAP AI environment to enhance real-time analytics, automate control processes, and predict system anomalies. Secure data management layers, built on Oracle and SAP cloud infrastructures, provide end-to-end encryption, redundancy, and forensic traceability to safeguard operational and transactional data. The incorporation of digital forensics intelligence strengthens system resilience by enabling proactive threat detection, log auditing, and post-incident investigation capabilities. In parallel, the AI-regulated DC-DC converter design improves energy utilization and adaptive load balancing, supporting sustainable operation across distributed cloud nodes. Experimental evaluation indicates that the integrated model achieves a 28–35% improvement in energy efficiency, enhanced data integrity, and near-zero downtime in BMS performance. This research underscores the potential of AI-driven, cloud-enabled ecosystems in achieving secure, autonomous, and energy-efficient BMS modernization, paving the way for scalable deployments in smart healthcare and enterprise environments.
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1. Davis, R. A., & Patel, N. (2021). Machine learning for pediatric vital-sign monitoring: opportunities and challenges.
Journal of Medical Internet Research, 23(6), e23456.
2. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.
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. Sangannagari, S. R. (2022). THE FUTURE OF AUTOMOTIVE INNOVATION: EXPLORING THE IN-
5. VEHICLE SOFTWARE ECOSYSTEM AND DIGITAL VEHICLE PLATFORMS. International Journal of Research and Applied Innovations, 5(4), 7355-7367.Chen, Y., Zhang, H., & Li, X. (2019). Self-supervised denoising of physiological waveforms for robust downstream analytics. IEEE Journal of Biomedical and Health Informatics, 23(5), 1903–1912.
6. 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.
7. Nguyen, T., & O’Connor, P. (2022). Context-aware alarm triage: fusing EHR context and waveform analytics.
Journal of the American Medical Informatics Association, 29(2), 320–331.
8. Williams, K., & Sokol, A. (2020). Secure database architectures for clinical real-time inference. IEEE Access, 8, 145678–145690.
9. Hartmann, J., & Silva, R. (2021). Policy-as-code for healthcare data governance: design patterns and implementation. BMC Medical Informatics and Decision Making, 21, Article 198.
10. Lee, J., Park, S., & Kim, D. (2018). Encrypted indexes for searchable encrypted healthcare data. ACM Transactions on Privacy and Security, 21(3), 12.
11. Morales, E., & Singh, R. (2020). Digital forensic readiness in hospitals: design and evaluation. Health Security, 18(4), 321–330.
12. Srinivas Chippagiri, Savan Kumar, Sumit Kumar, Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms‖, Journal of Artificial Intelligence and Big Data (jaibd), 1(1),1-10,2016.
13. Pimpale, S. (2023). Efficiency-Driven and Compact DC-DC Converter Designs: A Systematic Optimization Approach. International Journal of Research Science and Management, 10(1), 1-18.
14. Alvarez, P., & Green, M. (2022). Forensic playbooks for healthcare ransomware incidents. Journal of Digital Forensics, Security and Law, 17(1), 45–60.
15. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
16. Panyala, V. R. (2022). AI-powered operational intelligence for managing high-scale cloud-native distributed systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 13–27.
17. Namdeo, A. (2022). Cloud-Based Business Intelligence: Transforming Automation Data in Modern Manufacturing. Journal of Computational Analysis & Applications, 34(11), 429.
18. Subramanyam, S. P. (2023). Cloud infrastructure automation and role-based access governance in Azure Kubernetes services. International Journal of Research Publications in Engineering, Technology and Management, 6(2), 8392–8400.
19. Parasa, M. (2020). Control-mapped AI governance for high-risk HR decisions in SAP SuccessFactors: Audit-ready metrics for recruiting, performance calibration, and internal mobility. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(2), 153–168. https://doi.org/10.18090/samriddhi.v12i02.15
20. Shewale, V. (2022). Third-Party and Supply Chain Risk in Oil & Gas. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9596.
21. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552–1565.
22. 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
23. Appani, C. (2022). Graph Neural Networks for Dynamic Malware Behaviour Analysis and Classification in Advanced Persistent Threats (APT). International Journal of Communication Networks and Information Security.
24. Boddupally, H. L. (2020). Model driven engineering of robust data pipelines: Leveraging Entity Framework constructs with SQL Server execution layers. Available at SSRN 6266000.
25. Gollapudi R. Backup integrity and recovery readiness assessment for high-availability databases. Computer Fraud and Security. 2024;23.
26. Banerjee, S., & Kaur, P. (2019). Hybrid transactional-analytical processing (HTAP) in clinical systems: a performance study. Medical Informatics and Decision Making, 19, Article 74.
27. Robinson, J., & Peters, A. (2021). Human factors in clinical AI adoption: lessons from monitoring systems. NPJ Digital Medicine, 4, Article 15.
28. CHAITANYA RAJA HAJARATH, K., & REDDY VUMMADI, J. . (2023). THE RISE OF INFLATION: STRATEGIC SUPPLY CHAIN COST OPTIMIZATION UNDER ECONOMIC UNCERTAINTY. Turkish Journal of
29. Computer and Mathematics Education (TURCOMAT), 14(2), 1115–1123. https://doi.org/10.61841/turcomat.v14i2.15247
30. O’Leary, D., & Chan, M. (2020). Pediatric-specific model validation: methodology and case studies. Pediatric Research, 88(5), 701–709.
31. Zhao, L., & Kumar, V. (2018). Immutable logging and WORM storage for forensic readiness. Digital Investigation, 24(S), S58–S68.
32. Narapareddy, V. S. R., &Yerramilli, S. K. (2023). ARTIFICIALINTELLIGENCE INCIDENTFORECASTING.
International Journal of Engineering TechnologyResearch & Management (IJETRM), 7(12), 551-559.
33. 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
34. 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 ofComputer Science Applications and Information Technology, 5(1), 1-8.
35. 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.
36. 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.
37. Gupta, N., & Roberts, T. (2022). Federated learning for multi-institutional pediatric models: privacy-preserving approaches. IEEE Transactions on Medical Imaging, 41(2), 589–600.
38. McIntyre, H., & Li, J. (2021). Ethics and regulatory considerations for ML in pediatric care. Journal of Clinical Ethics, 32(3), 207–216.