Cognitive JavaScript Cloud Framework: Real-Time Risk Detection and Adaptive Security using Neural Networks and Machine Learning
DOI:
https://doi.org/10.15680/IJCTECE.2024.0705004Keywords:
AI augmented software development, cloud native ERP, SAP HANA, digital payment automation, machine learning, software engineering framework, DevOps for ERPAbstract
This paper proposes an AI‑augmented software development framework aimed at enabling cloud‑native enterprise resource planning (ERP) systems that integrate digital payment automation, leveraging the in‑memory database platform SAP HANA and machine‑learning models. The framework envisions a layered architecture in which AI‑driven modules support automated payment workflows (capture, reconciliation, fraud detection), embedded within a cloud‑native ERP environment. By aligning software development practices (code generation, testing automation, continuous integration) with AI‑augmented capabilities, the proposed approach seeks to accelerate development time, ensure higher quality and reliability, and better adapt to evolving payment ecosystems. The cloud‑native nature of the ERP platform enables elasticity, micro‑service modularization, and modern DevOps pipelines, while SAP HANA provides real‑time transaction analytics, machine‑learning model embedding, and seamless enterprise integration. We articulate the research design, describe the conceptual model, and discuss hypothetical evaluation results showing improvements in development cycle‑time reduction, defect rates, payment processing latency and automation coverage. The paper also examines the advantages (speed, scalability, integration) and disadvantages (complexity, cost, governance) of the framework. Ultimately, we conclude that AI‑augmented software development frameworks for cloud‑native ERP environments hold substantial promise for digital‑payment automation, and we highlight directions for future research, including cross‑domain reuse, adaptive ML models, and edge‑cloud hybrids.
References
1. Arul Raj A. M., Sugumar R. (2024). Detection of Covid-19 based on convolutional neural networks using pre-processed chest X-ray images (14th edition). Aip Advances 14 (3):1-11.
2. 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.
3. Kesavan, E. (2023). Codeless Automation Versus Scripting: A Case Study on Selenium-Based JavaScript Testing Tools. International Journal of Scientific Research and Modern Technology, 2(5), 7-14. https://ideas.repec.org/a/daw/ijsrmt/v2y2023i5p7-14id843.html
4. Rawat, C. (2023). Role of ERP Modernization in Digital Transformation: PeopleSoft Insight. arXiv:2303.03224.
5. Zhao, L., Wang, Q., Wang, C., Li, Q., Shen, C., Lin, X., Hu, S., & Du, M. (2019). VeriML: Enabling integrity assurances and fair payments for machine learning as a service. arXiv. https://arxiv.org/abs/1909.06961 arXiv
6. Bussu, V. R. R. Leveraging AI with Databricks and Azure Data Lake Storage. https://pdfs.semanticscholar.org/cef5/9d7415eb5be2bcb1602b81c6c1acbd7e5cdf.pdf
7. Lucas, Y., Portier, P. E., Laporte, L., He Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2019). Towards automated feature engineering for credit card fraud detection using multi perspective HMMs. arXiv. https://arxiv.org/abs/1909.01185 arXiv
8. Christadoss, J., & Mani, K. (2024). AI-Based Automated Load Testing and Resource Scaling in Cloud Environments Using Self-Learning Agents. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 604-618.
9. Bhatia, R. (2023). The impact of SAP Business Technology Platform (BTP) on financial data analytics and reporting. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/CSEIT251112140
10. GUPTA, A. B., et al. (2023). "Smart Defense: AI-Powered Adaptive IDs for Real-Time Zero-Day Threat Mitigation."
11. Bhowmik, L., & Dhar, A. (2021). Machine Learning with SAP Models and Applications. SAP PRESS. (Note: this is essentially the same as reference #1 but emphasises models/applications)
12. Veershetty, G. (2023). Risk-Adaptive Transition and Transformation (RATT): A Predictive Governance Framework for SAP Cloud Migration Programs.
13. Adepu, R. (2023). Designing FedRAMP-Compliant Cloud Architectures for Secure and Scalable Government Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 10427-10441.
14. Sarabu, V. B. (2018). A framework-driven approach to data validation and reconciliation for operational accuracy. International Journal of Research and Applied Innovations, 1(1), 2130-2140.
15. Adepu, G. (2023). Large Language Model–Powered Public Service Platforms for Automated Case Assistance and Decision Support. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7744-7748.
16. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
17. Katta, T. B. (2022). A Capability Maturity Framework for Event-Driven Integration: Benchmarking Kafka and Pulsar in Enterprise Environments. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9589.
18. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
19. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
20. Shewale, V. (2023). AI and Machine Learning for Anomaly Detection in ICS Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(3), 11631.
21. Parasa, M. (2021). Encryption-aware data integrity and quality controls in SAP SuccessFactors integrations using machine learning and cryptographic hash chains for tamper detection. International Journal of Computer Technology and Electronics Communication, 4(6), 4304–4316. https://doi.org/10.15680/IJCTECE.2021.0406014
22. Subramanyam, S. P. (2022). Kubernetes-oriented continuous deployment architecture for .NET microservices. International Journal of Future Innovative Science and Technology (IJFIST), 5(3), 8482–8490. https://doi.org/10.15662/IJFIST.2022.0503002
23. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715–3724.
24. Panyala, V. R. (2023). Revolutionary leadership in architecting cloud-native platforms for high-volume transaction processing. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 63–79.
25. Prasad, P. K. (2022). Platform engineering & FinOps: The next frontier of cloud optimization. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(6), 16244–16253. https://doi.org/10.15680/IJCTECE.2022.0506025
26. Vayyasi, N. K. (2023). Optimizing factory maintenance and downtime prediction through Java-driven AI pipelines. International Journal of Research and Applied Innovations (IJRAI), 6(3).
27. Kavuru, Lakshmi Triveni. (2023). Agile Management Outside Tech: Lessons from Non-IT Sectors. International Journal of Multidisciplinary Research in Science Engineering and Technology. 10.15680/IJMRSET.2023.0607052.
28. Anbalagan, B., & Pasumarthi, A. (2022). Building Enterprise Resilience through Preventive Failover: A Real-World Case Study in Sustaining Critical Sap Workloads. International Journal of Computer Technology and Electronics Communication, 5(4), 5423-5441.
29. Sridhar Kakulavaram. (2024). Artificial Intelligence-Driven Frameworks for Enhanced Risk Management in Life Insurance. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4873–4897. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2996
30. 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.
31. Peddamukkula, P. K. Ethical Considerations in AI and Automation Integration Within the Life Insurance Industry. https://www.researchgate.net/profile/Praveen-Peddamukkula/publication/397017494_Ethical_Considerations_in_AI_and_Automation_Integration_Within_the_Life_Insurance_Industry/links/690239c04baee165918ee584/Ethical-Considerations-in-AI-and-Automation-Integration-Within-the-Life-Insurance-Industry.pdf
32. Kotapati, V. B. R., & Yakkanti, B. (2023). Real-Time Analytics Optimization Using Apache Spark Structured Streaming: A Lambda Architecture-based Scala Framework. American Journal of Data Science and Artificial Intelligence Innovations, 3, 86-119.
33. Arulraj AM, Sugumar, R., Estimating social distance in public places for COVID-19 protocol using region CNN, Indonesian Journal of Electrical Engineering and Computer Science, 30(1), pp.414-424, April 2023
34. Sankar, Thambireddy,. (2024). SEAMLESS INTEGRATION USING SAP TO UNIFY MULTI-CLOUD AND HYBRID APPLICATION. International Journal of Engineering Technology Research & Management (IJETRM), 08(03), 236–246. https://doi.org/10.5281/zenodo.15760884
35. 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.
36. Sivaraju, P. S., & Mani, R. (2024). Private Cloud Database Consolidation in Financial Services: A Comprehensive Case Study on APAC Financial Industry Migration and Modernization Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10472-10490.
37. 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.
38. Kandula, N. Optimizing Image Processing in OmniView with EDAS Decision-Making. https://www.researchgate.net/profile/Nagababu-Kandula/publication/393517053_Optimizing_Image_Processing_in_OmniView_with_EDAS_Decision-Making/links/686e55e392697d42903df70b/Optimizing-Image-Processing-in-OmniView-with-EDAS-Decision-Making.pdf
39. 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).
40. Hammer, M., & Champy, J. (1993). Reengineering the Corporation: A Manifesto for Business Revolution. HarperBusiness. (Foundational work for business process & ERP thinking)
