Articles
An AI-Cloud Converged Architecture for Cybersecurity Fraud Analytics and Medical Imaging over 5G with Oracle EBS and Unified Payment Orchestration
Abstract
The rapid convergence of artificial intelligence (AI), cloud computing, and high-speed 5G networks is transforming secure enterprise and healthcare digital ecosystems. This paper presents an AI-cloud converged architecture designed to support cybersecurity threat detection, financial fraud analytics, and medical image analysis in broadband and 5G environments, while ensuring seamless enterprise integration through Oracle E-Business Suite (EBS) and a unified payment orchestration platform. The proposed architecture leverages scalable cloud-native microservices, API-driven interoperability, and real-time AI inference to enable adaptive cyber defense mechanisms, intelligent fraud detection across multi-channel payment systems, and high-performance medical imaging workflows. Advanced machine learning and deep learning models are employed for anomaly detection, identity verification, and clinical image interpretation, benefiting from low-latency 5G connectivity for real-time data exchange. Security is embedded across all layers through zero-trust principles, encryption, continuous monitoring, and regulatory compliance controls. Integration with Oracle EBS enables synchronized financial, operational, and risk management processes, while payment orchestration ensures secure and resilient transaction processing. The proposed architecture demonstrates how AI-cloud convergence can deliver unified intelligence, enhanced security, and operational efficiency across financial and healthcare domains in next-generation network environments.
Article Information
Journal |
International Journal of Future Innovative Science and Technology (IJFIST) |
|---|---|
Volume (Issue) |
Vol. 6 No. 4 (2023): International Journal of Future Innovative Science and Technology (IJFIST) |
DOI |
|
Pages |
10975-10985 |
Published |
July 13, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sophie Anna Bakker (2023). An AI-Cloud Converged Architecture for Cybersecurity Fraud Analytics and Medical Imaging over 5G with Oracle EBS and Unified Payment Orchestration. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 6 No. 4 (2023): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 10975-10985. https://doi.org/10.15662/194 |
References
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5. 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.
6. Borra, C. R. (2022). A Comparative Study of Privacy Policies in E-Commerce Platforms. International Journal of Research and Applied Innovations, 5(3), 7065-7069.
7. D. Johnson, L. Ramamoorthy, J. Williams, S. Mohamed Shaffi, X. Yu, A. Eberhard, S. Vengathattil, and O. Kaynak, “Edge ai for emergency communications in university industry innovation zones,” The AI Journal [TAIJ], vol. 3, no. 2, Apr. 2022.
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9. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
10. Ramidi, M. (2022). Building secure biometric systems for digital identity verification in aviation mobile apps. International Journal of Engineering & Extended Technologies Research, 4(4), 5036–5047.
11. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
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13. Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining (pp. 413–422).
14. Russakovsky, O., Deng, J., Su, H., et al. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252
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17. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
18. Singh, A. (2023). Network slicing and its testing in 5G networks. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8005–8013. https://doi.org/10.15680/IJCTECE.2023.0606020
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24. Manda, P. (2023). LEVERAGING AI TO IMPROVE PERFORMANCE TUNING IN POST-MIGRATION ORACLE CLOUD ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8714-8725.
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