A Unified AI–Cloud Architecture for Healthcare, Finance, and Agriculture Leveraging ML, NLP, and Disease Analytics
DOI:
https://doi.org/10.15662/IJEETR.2025.0706016Keywords:
AI–Cloud Framework, Machine Learning (ML), Natural Language Processing (NLP), Open Banking APIs, Healthcare Analytics, Agricultural Disease Prediction, Cotton Leaf Disease Detection, Cloud Computing, Multi-Cloud Architecture, Federated Learning, Sustainable Digital EcosystemsAbstract
The rapid proliferation of digital ecosystems across healthcare, finance, and agriculture demands unified, intelligent, and scalable architectures capable of delivering sustainable, data-driven decision support. This study proposes a Unified AI–Cloud Framework that integrates Machine Learning (ML), Natural Language Processing (NLP), Open Banking APIs, and agricultural disease analytics within a secure, multi-cloud environment. The framework leverages cloud-native microservices, real-time data pipelines, federated learning models, and secure API gateways to enable cross-sector interoperability. In healthcare, ML-driven clinical prediction and NLP-based patient record mining enhance diagnostic accuracy and operational efficiency. In finance, Open Banking integration supports intelligent credit scoring, transaction anomaly detection, and personalized risk-aware services. In agriculture, convolutional neural networks (CNNs) and spectral analytics are used for early detection of crop diseases such as cotton leaf disease. The proposed architecture emphasizes sustainability through energy-efficient model deployment, privacy-preserving data governance, and adaptive resource provisioning. Experimental results and simulations demonstrate improved decision accuracy, reduced latency, and enhanced system resilience across all three domains. The framework establishes a scalable foundation for future AI-driven, cross-industry digital transformation.
References
1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT.
2. Manivannan, R., Sugumar, R., & Vijayabharathi, R. (2025, May). A Convolutional Deep Learning Method for Digital Image Processing in the Identification of Vitamin Deficiencies. In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) (pp. 1-6). IEEE.
3. Kumar, R., Christadoss, J., & Soni, V. K. (2024). Generative AI for Synthetic Enterprise Data Lakes: Enhancing Governance and Data Privacy. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 351-366.
4. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
5. 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.
6. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
7. McMahan, B., et al. (2017). Communication-efficient learning of deep networks from decentralized data. AISTATS.
8. Peram, S. R. (2025). Machine Learning-Based performance evaluation and memory usage forecasting for intelligent systems. Journal of Artificial Intelligence and Machine Learning, 3(3), 275. https://www.researchgate.net/profile/Sudhakara-Peram/publication/395586137_Machine_Learning-Based_Performance_Evaluation_and_Memory_Usage_Forecasting_for_Intelligent_Systems/links/68cbbd13d221a404b2a0abbf/Machine-Learning-Based-Performance-Evaluation-and-Memory-Usage-Forecasting-for-Intelligent-Systems.pdf
9. Kusumba, S. (2025). Empowering Federal Efficiency: Building an Integrated Maintenance Management System (Imms) Data Warehouse for Holistic Financial And Operational Intelligence. Journal Of Multidisciplinary, 5(7), 377-384.
10. Joseph, J. (2025). The Protocol Genome A Self Supervised Learning Framework from DICOM Headers. arXiv preprint arXiv:2509.06995. https://arxiv.org/abs/2509.06995
11. Muthusamy, P., Thangavelu, K., & Bairi, A. R. (2023). AI-Powered Fraud Detection in Financial Services: A Scalable Cloud-Based Approach. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 146-181.
12. Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis. Radiology, 284(2), 574–582.
13. Rahman MM, Najmul Gony M, Rahman MM, Rahman MM, Maria Khatun Shuvra SD. Natural language processing in legal document analysis software: A systematic review of current approaches, challenges, and opportunities. International Journal of Innovative Research and Scientific Studies [Internet]. 2025 Jun 10 [cited 2025 Aug 25];8(3):5026–42. Available from: https://www.ijirss.com/index.php/ijirss/article/view/7702
14. 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
15. Zhang, Y., Xu, L., & Zhao, H. (2020). Transformer-based financial document analysis. Expert Systems with Applications, 147, 113215.
16. Konda, S. K. (2024). AI Integration in Building Data Platforms: Enabling Proactive Fault Detection and Energy Conservation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3), 10327-10338.
17. Sardana, A., Kotapati, V. B. R., & Ponnoju, S. C. (2025). Autonomous Audit Agents for PCI DSS 5.0: A Reinforcement Learning Approach. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(1), 130-136.
18. Kumar, R. K. (2022). AI-driven secure cloud workspaces for strengthening coordination and safety compliance in distributed project teams. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8075–8084. https://doi.org/10.15662/IJRAI.2022.0506017
19. Dendukuri, S. V. (2025). Federated Learning in Healthcare: Protecting Patient Privacy While Advancing Analytics. Journal of Computer Science and Technology Studies, 7(7), 840-845.
20. Kumar, S. N. P. (2025). Scalable Cloud Architectures for AI-Driven Decision Systems. Journal of Computer Science and Technology Studies, 7(8), 416-421.
21. Dharmateja Priyadarshi Uddandarao. (2024). Counterfactual Forecastingof Human Behavior using Generative AI and Causal Graphs. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 5033 –. Retrievedfrom https://ijisae.org/index.php/IJISAE/article/view/7628
22. Sivaraju, P. S. (2024). Driving Operational Excellence Via Multi-Market Network Externalization: A Quantitative Framework for Optimizing Availability, Security, And Total Cost in Distributed Systems. International Journal of Research and Applied Innovations, 7(5), 11349-11365.
23. Vasugi, T. (2023). AI-empowered neural security framework for protected financial transactions in distributed cloud banking ecosystems. International Journal of Advanced Research in Computer Science & Technology, 6(2), 7941–7950. https://doi.org/0.15662/IJARCST.2023.0602004
24. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2024). Artificial Neural Network in Fibre-Reinforced Polymer Composites using ARAS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 9801-9806.
25. Kandula, N. Machine Learning Approaches to Predict Tensile Strength in Nanocomposite Materials a Comparative Analysis.
26. Pasumarthi, A. (2023). Dynamic Repurpose Architecture for SAP Hana Transforming DR Systems into Active Quality Environments without Compromising Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6263-6274.
27. Kesavan, E., Srinivasulu, S., & Deepak, N. M. (2025, July). Cloud Computing for Internet of Things (IoT): Opportunities and Challenges. In 2025 2nd International Conference on Computing and Data Science (ICCDS) (pp. 1-6). IEEE.
28. Nagarajan, G. (2022). An integrated cloud and network-aware AI architecture for optimizing project prioritization in healthcare strategic portfolios. International Journal of Research and Applied Innovations, 5(1), 6444–6450. https://doi.org/10.15662/IJRAI.2022.0501004
29. Sugumar, R. (2025, March). Diabetes Insights: Gene Expression Profiling with Machine Learning and NCBI Datasets. In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS) (pp. 712-718). IEEE.
30. Muthusamy, M. (2024). Cloud-Native AI metrics model for real-time banking project monitoring with integrated safety and SAP quality assurance. International Journal of Research and Applied Innovations (IJRAI), 7(1), 10135–10144. https://doi.org/10.15662/IJRAI.2024.0701005
31. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
32. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
