Fault Tolerant AI–Cloud Architecture for Healthcare, Finance, and Agriculture: ML, NLP, and Disease Analytics Integrated with SAP ERP
DOI:
https://doi.org/10.15662/IJRAI.2024.0706026Keywords:
fault-tolerant architecture, cloud AI, ERP integration, healthcare analytics, financial fraud detection, agriculture analytics, machine learning, NLP, data governance, metadata lineageAbstract
The acceleration of digital transformation across healthcare, finance, and agriculture has led to integrated ERP (Enterprise Resource Planning) deployments increasingly reliant on real‑time data flows, machine learning, and cloud-based analytics. However, this convergence introduces new vulnerabilities: data inconsistencies, system outages, fraud, disease outbreak misclassification, and regulatory compliance failures. This paper proposes a fault‑tolerant, AI–cloud architecture that unifies ML-driven analytics (for credit card fraud, anomaly detection, and disease outbreak analytics), Natural Language Processing (NLP) for unstructured data (e.g., medical notes, financial transaction narratives, agricultural sensor logs), and robust metadata lineage and governance via an enhanced ERP backbone (e.g., built on SAP HANA or an equivalent ERP platform). The design emphasizes redundancy, fail‑over, real-time streaming ingestion, model versioning, and explainability to ensure continuous, trustworthy operation across domains. We outline a data ingestion and processing pipeline, fault‑tolerance mechanisms (e.g., distributed compute, fallback to rule-based logic), combined ML/NLP models for multiple use-cases, a unified decision engine, and a governance & audit layer. Simulated experiments show that the architecture maintains high availability (> 99.9%), yields improved detection precision and recall over standalone systems, and ensures traceability for compliance audits. We conclude by discussing trade‑offs, deployment considerations, and future work toward federated learning and cross-domain knowledge sharing.References
1. Anderson, J., & Brown, M. (2015). Cloud-native fault-tolerant architectures for enterprise applications. Journal of Cloud Computing, 4(2), 45–59.
2. Udayakumar, R., Joshi, A., Boomiga, S. S., & Sugumar, R. (2023). Deep fraud Net: A deep learning approach for cyber security and financial fraud detection and classification. Journal of Internet Services and Information Security, 13(3), 138-157.
3. Harish, M., & Selvaraj, S. K. (2023, August). Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach. In AIP Conference Proceedings (Vol. 2790, No. 1, p. 020021). AIP Publishing LLC.
4. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
5. Abdul Salam Abdul Karim. (2023). Fault-Tolerant Dual-Core Lockstep Architecture for Automotive Zonal Controllers Using NXP S32G Processors. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 877–885. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7749
6. Md, A. R. (2023). Machine learning–enhanced predictive marketing analytics for optimizing customer engagement and sales forecasting. International Journal of Research and Applied Innovations (IJRAI), 6(4), 9203–9213. https://doi.org/10.15662/IJRAI.2023.0604004
7. Uddandarao, D. P., & Vadlamani, R. K. (2025). Counterfactual Forecasting of Human Behavior using Generative AI and Causal Graphs. arXiv preprint arXiv:2511.07484.
8. Navandar, Pavan. "Enhancing Cybersecurity in Airline Operations through ERP Integration: A Comprehensive Approach." Journal of Scientific and Engineering Research 5, no. 4 (2018): 457-462.
9. Mani, R. (2022). Enhancing SAP HANA Resilience and Performance on RHEL using Pacemaker: A Strategic Approach to Migration Optimization and Dual-Function Infrastructure Design. International Journal of Computer Technology and Electronics Communication, 5(6), 6061-6074.
10. Sivaraju, P. S. (2023). Thin client and service proxy architectures for X systems in distributed operations. International Journal of Advanced Research in Computer Science & Technology, 6(6), 9510–9515.
11. Das, D., Vijayaboopathy, V., & Rao, S. B. S. (2018). Causal Trace Miner: Root-Cause Analysis via Temporal Contrastive Learning. American Journal of Cognitive Computing and AI Systems, 2, 134-167.
12. Zubair, K. M., Akash, T. R., & Chowdhury, S. A. (2023). Autonomous Threat Intelligence Aggregation and Decision Infrastructure for National Cyber Defense. Frontiers in Computer Science and Artificial Intelligence, 2(2), 26-51.
13. A. K. S, L. Anand and A. Kannur, "A Novel Approach to Feature Extraction in MI - Based BCI Systems," 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, 2024, pp. 1-6, doi: 10.1109/CSITSS64042.2024.10816913.
14. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR).
15. Koh, P. W., & Liang, P. (2017). Understanding black-box predictions via influence functions. In Proceedings of the 34th International Conference on Machine Learning (pp. 1885–1894).
16. Kumar, A., & Patel, N. (2019). Combining time-series and NLP models for supply-chain anomaly detection. International Journal of Supply Chain Analytics, 6(4), 210–225.
17. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning, nature, 521(7553), 436–444.
18. Li, X., Zhao, Y., & Wang, J. (2022). Federated learning for multi-institution healthcare data: challenges and opportunities. Journal of Biomedical Informatics, 120, 103809.
19. Kandula N (2023). Gray Relational Analysis of Tuberculosis Drug Interactions A Multi-Parameter Evaluation of Treatment Efficacy. J Comp Sci Appl Inform Technol. 8(2): 1-10.
20. Sridhar Reddy Kakulavaram, Praveen Kumar Kanumarlapudi, Sudhakara Reddy Peram. (2024). Performance Metrics and Defect Rate Prediction Using Gaussian Process Regression and Multilayer Perceptron. International Journal of Information Technology and Management Information Systems (IJITMIS), 15(1), 37-53.
21. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2024). Evaluation of crime rate prediction using machine learning and deep learning for GRA method. Data Analytics and Artificial Intelligence, 4 (3).
22. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144).
23. Sculley, D., et al. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems (pp. 2503–2511).
24. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
25. Singh, N., & Verma, R. (2020). IoT-based sensor networks for precision agriculture: a review. International Journal of Agricultural Technology, 16(3), 587–602.
26. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
27. Thangavelu, K., Keezhadath, A. A., & Selvaraj, A. (2022). AI-Powered Log Analysis for Proactive Threat Detection in Enterprise Networks. Essex Journal of AI Ethics and Responsible Innovation, 2, 33-66.
28. Panguluri, L. D., Mohammed, S. B., & Pichaimani, T. (2023). Synthetic Test Data Generation Using Generative AI in Healthcare Applications: Addressing Compliance and Security Challenges. Cybersecurity and Network Defense Research, 3(2), 280-319.
29. Kumar, R. K. (2023). AI‑integrated cloud‑native management model for security‑focused banking and network transformation projects. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9321–9329. https://doi.org/10.15662/IJRPETM.2023.0605006
30. Muthusamy, M. (2022). AI-Enhanced DevSecOps architecture for cloud-native banking secure distributed systems with deep neural networks and automated risk analytics. International Journal of Research Publication and Engineering Technology Management, 6(1), 7807–7813. https://doi.org/10.15662/IJRPETM.2022.0506014
31. Nagarajan, G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication, 5(2), 4812–4820. https://doi.org/10.15680/IJCTECE.2022.0502003
32. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
33. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
34. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
35. Zhao, P., & Liu, J. (2024). Scalable metadata governance frameworks for enterprise data platforms. Data Engineering Journal, 11(1), 5–21.
