Next-Generation Cloud Software Development: Reinforcement Learning and Ethical AI Synergy with NLP-Based Cognitive Governance
DOI:
https://doi.org/10.15680/IJCTECE.2020.0405004Keywords:
Artificial Intelligence, Oracle Cloud Infrastructure, SAP Business Data Intelligence, Healthcare Data Management, Decision Support Systems, Machine Learning, Interoperability, Predictive AnalyticsAbstract
The increasing complexity of healthcare data demands advanced digital infrastructures capable of managing and interpreting large volumes of information in real time. Traditional healthcare systems, often reliant on siloed databases, struggle to deliver accurate insights required for timely clinical decision-making. This research introduces an AI-driven Oracle-SAP hybrid framework for intelligent healthcare data management and decision support. By integrating Oracle Cloud Infrastructure (OCI) and SAP Business Data Intelligence (BDI), the framework unifies healthcare operations, finance, and clinical analytics under one intelligent architecture. The AI components—machine learning (ML) and deep learning (DL) models—enhance data quality, automate predictive analytics, and provide clinicians and administrators with actionable insights.
The proposed hybrid model combines Oracle’s robust transactional processing with SAP’s powerful analytics to facilitate end-to-end interoperability, enabling real-time patient monitoring, resource optimization, and diagnostic accuracy. AI algorithms are integrated for anomaly detection, patient readmission prediction, and workflow automation. The system also ensures compliance with healthcare standards such as HIPAA and GDPR.
Experimental results from simulated hospital datasets demonstrate a 45% improvement in data accessibility, a 37% enhancement in predictive accuracy, and a 33% reduction in operational costs compared to traditional ERP-based solutions. Furthermore, the hybrid Oracle-SAP framework provides scalable and secure integration of structured and unstructured data, empowering healthcare providers to transition toward proactive, data-driven decision-making. Overall, the study underscores how an AI-driven Oracle-SAP hybrid ecosystem can modernize healthcare data management while ensuring operational intelligence and sustainability.
References
1. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095
2. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
3. M.Sabin Begum, R.Sugumar, "Conditional Entropy with Swarm Optimization Approach for Privacy Preservation of Datasets in Cloud", Indian Journal of Science and Technology, Vol.9, Issue 28, July 2016
4. Anand, L., Nallarasan, V., Krishnan, M. M., & Jeeva, S. (2020, October). Driver profiling-based anti-theft system. In AIP Conference Proceedings (Vol. 2282, No. 1, p. 020042). AIP Publishing LLC.
5. Dignum, V. (2018). Ethics in artificial intelligence: Introduction to the special issue. Ethics and Information Technology, 20(1), 1–3. https://doi.org/10.1007/s10676-018-9450-z
6. Vengathattil, S. (2019). Ethical Artificial Intelligence - Does it exist? International Journal for Multidisciplinary Research, 1(3). https://doi.org/10.36948/ijfmr.2019.v01i03.37443
7. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1), 1–15. https://doi.org/10.1162/99608f92.8cd550d1
8. Cherukuri, B. R. (2020). Ethical AI in cloud: Mitigating risks in machine learning models.
9. Anand, L., Krishnan, M. M., Senthil Kumar, K. U., & Jeeva, S. (2020, October). AI multi agent shopping cart system based web development. In AIP Conference Proceedings (Vol. 2282, No. 1, p. 020041). AIP Publishing LLC.
10. 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.
11. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285. https://doi.org/10.1613/jair.301
12. Kreutz, D., Ramos, F. M. V., Verissimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76. https://doi.org/10.1109/JPROC.2014.2371999
13. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
14. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (Special Publication 800-145). National Institute of Standards and Technology.
15. Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Farrar, Straus and Giroux.
16. Salehi, M., & Goudarzi, M. (2016). Resource-aware cloud computing: Techniques and challenges. Computers & Electrical Engineering, 51, 151–166. https://doi.org/10.1016/j.compeleceng.2015.10.015
17. Shailendra, S., & Kumar, P. (2018). Cognitive software-defined networks: A new paradigm for intelligent cloud computing. International Journal of Computer Networks & Communications, 10(3), 23–34. https://doi.org/10.5121/ijcnc.2018.10303
18. Rengarajan A, Sugumar R and Jayakumar C (2016) Secure verification technique for defending IP spoofing attacks Int. Arab J. Inf. Technol., 13 302-309
19. Srinivas Chippagiri , Savan Kumar, Olivia R Liu Sheng,‖ Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media‖, Journal of Artificial Intelligence and Big Data(jaibd),1(1),11-20,2016.
20. van Wynsberghe, A. (2019). Designing robots for care: Care centered value-sensitive design. Science and Engineering Ethics, 25(4), 875–893. https://doi.org/10.1007/s11948-018-0035-6
21. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18. https://doi.org/10.1007/s13174-010-0007-6
22. Mohammed, A. A., Akash, T. R., Zubair, K. M., & Khan, A. (2020). AI-driven Automation of Business rules: Implications on both Analysis and Design Processes. Journal of Computer Science and Technology Studies, 2(2), 53-74.
