Cross-Domain Cloud Architecture with AI Integration for Public Biomedical Systems and Secure Enterprise Automation
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806027Keywords:
AI-driven cloud architecture, cross-domain systems, biomedical systems, enterprise automation, secure digital access, equitable technology, public sector IT, cloud computingAbstract
Public sector biomedical systems increasingly rely on cloud computing and AI technologies to process, analyze, and manage large-scale health data, while enterprise automation requires integration across heterogeneous systems. This paper proposes a cross-domain, AI-driven cloud architecture designed to support biomedical data processing, enterprise automation, and secure, equitable digital access. The architecture leverages AI for predictive analytics, process optimization, anomaly detection, and decision support. Cloud infrastructure enables scalability, fault tolerance, and efficient resource utilization, while microservices and containerization facilitate modularity and interoperability. The framework incorporates advanced security mechanisms, including role-based access control, encryption, and identity management, ensuring compliance with privacy regulations and equitable access to services across socio-economic and geographic demographics. The study evaluates the architecture in terms of performance, reliability, security, and accessibility. It highlights challenges such as heterogeneous data integration, interoperability, AI model bias, and regulatory compliance. Advantages include improved operational efficiency, data-driven decision-making, and enhanced accessibility. Disadvantages involve computational complexity, potential AI bias, and resource-intensive infrastructure. The proposed AI-driven cross-domain cloud architecture provides a blueprint for integrating public sector biomedical systems with enterprise automation, offering secure, scalable, and equitable digital services for diverse populations.
References
1. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-Learning Scheduler for Multi-Tenant Spark Clustersunder Privacy Constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496-527.
2. Patnaik, S. K., Sidhu, M. S., Gehlot, Y., Sharma, B., & Muthu, P. (2018). Automated skin disease identification using deep learning algorithm. Biomedical & Pharmacology Journal, 11(3), 1429.
3. Ponugoti, M. (2023). Bridging the digital divide: Architecture for equitable technological access. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6991–7002.
4. Keezhadath, A. A., Kota, R. K., & Selvaraj, A. (2021). Dynamic Pricing Optimization for Global Hospitality: Real-Time Data Integration and Decision Making. American Journal of Autonomous Systems and Robotics Engineering, 1, 131-165.
5. Kamadi, S. (2022). Adaptive Federated Data Science & MLOps Architecture: A Comprehensive Framework for Distributed Machine Learning Systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 8(6), 745-755.
6. Gurajapu, A., & Garimella, V. (2025). Edge-to-cloud workflows for low-latency telecom services: Optimizing offload decisions. International Journal of Research and Applied Innovations (IJRAI), 8(4), 12638–12641.
7. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust Image Encryption in Transform Domain Using Duo Chaotic Maps—A Secure Communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271-281). Singapore: Springer Singapore.
8. Sriramoju, S. (2024). An API-driven solution for enhancing employee lifecycle and cost management efficiency. International Journal of Humanities and Information Technology (IJHIT), 6(3), 50–69. https://www.ijhit.info
9. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
10. Mulla, F. (2024). Choosing the Best Architecture for Mobile Applications. International Journal Of Research In Computer Applications And Information Technology, 7, 2350–2363. https://doi.org/10.34218/IJRCAIT_07_02_173
11. Kalabhavi, V. (2025). MIDDLEWARE RESILIENCE FRAMEWORK FOR SAP ECC-CRM INTEGRATION: DESIGN AND EVALUATION. International Journal of Applied Mathematics, 38(5s), 10-32.
12. Ananth, S., Radha, K., & Raju, S. (2024). Animal Detection In Farms Using OpenCV In Deep Learning. Advances in Science and Technology Research Journal, 18(1), 1.
13. Ramidi, M. (2022). Developing resilient offline-first architectures for mobile health and clinical research applications. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(1), 4518–4529.
14. Genne, S. (2023). Optimizing user experience in high-traffic financial web applications using analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7231–7241.
15. Surisetty, L. S. (2025). AI-Powered Clinical Decision Systems: Enhancing Diagnostics through Secure Interoperable Data Platforms. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(5), 12924-12932.
16. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
17. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
18. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
19. Paul, D., Namperumal, G., & Surampudi, Y. (2023). Optimizing llm training for financial services: best practices for model accuracy, risk management, and compliance in ai-powered financial applications. Journal of Artificial Intelligence Research and Applications, 3(2), 550-588.
20. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
21. Mudunuri, P. R. (2022). Automating compliance in biomedical DevOps: A policy-as-code approach. International Journal of Research and Applied Innovations (IJRAI), 5(2), 6770–6783.
22. Islam, M. M., Hasan, S., Rahman, K. A., Zerine, I., Hossain, A., & Doha, Z. (2024). Machine Learning model for Enhancing Small Business Credit Risk Assessment and Economic Inclusion in the United State. Journal of Business and Management Studies, 6(6), 377-385.
23. Gaddapuri, N. S. (2022). APPLICATION OF QUANTUM COMPUTING IN DIGITAL EDUCATION SYSTEMS. Power System Protection and Control, 50(2), 12-24.
24. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
25. Adari, Vijay Kumar, “Interoperability and Data Modernization: Building a Connected Banking Ecosystem,” International Journal of Computer Engineering and Technology (IJCET), vol. 15, no. 6, pp.653-662, Nov-Dec 2024. DOI:https://doi.org/10.5281/zenodo.14219429.
26. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
27. Chennamsetty, C. S. (2023). Standardizing Software Delivery: Unified Data Models and Scalable Infrastructure for Subscription Ecosystems. International Journal of Computer Technology and Electronics Communication, 6(2), 6658-6665.
28. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-8). IEEE.
29. Anumula, S. R. (2022). Transparent and auditable decision-making in enterprise platforms. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7691–7702. https://doi.org/10.15662/IJRAI.2022.0505007
30. Sardana, A., Das, D., & Mohammed, A. S. (2018). Swarm Agent Chaos Engineering for Autonomous Resiliency Assurance. Artificial Intelligence, Machine Learning, and Autonomous Systems, 2, 33-63.
31. Meshram, A. K. (2025). Real-time financial fraud prediction using big data streaming on cloud platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12834-12845.
32. Sikarwar, V. (2025). AI-Powered Process Mining for Intelligent, Personalized Customer Experience in the Insurance Sector. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12418-12428.
33. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
34. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
35. Ananth, S., & Saranya, A. (2016, January). Reliability enhancement for cloud services-a survey. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
36. Ahuja, D. (2025). DevOps and Ethical AI: Ensuring Responsible Deployment. Journal Of Multidisciplinary, 5(6), 1-14.
37. Raj, A. M. A., Rajendran, S., & Vimal, G. S. A. G. (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection. Bulletin of Electrical Engineering and Informatics, 13(3), 1935-1942.
38. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
