Secure and Intelligent Targeted Advertising for Healthcare ERP Platforms: An LLM-Enabled Cloud and Web Engineering Approach

Felix Reinhard Blumenthal

doi:10.15662/IJRAI.2022.0505005

Authors

Felix Reinhard Blumenthal Senior Software Engineer, Germany Author

DOI:

https://doi.org/10.15662/IJRAI.2022.0505005

Keywords:

Healthcare ERP, Targeted Advertising, Large Language Models (LLMs), Cloud Computing, Web Engineering, Data Security, Privacy-Preserving Analytics, Digital Marketing Analytics, Intelligent Systems

Abstract

The rapid adoption of Healthcare Enterprise Resource Planning (ERP) platforms has created new opportunities for data-driven and targeted digital advertising. However, healthcare environments demand high standards of security, privacy, and regulatory compliance. This paper proposes a secure and intelligent targeted advertising framework for healthcare ERP platforms using a Large Language Model (LLM)–enabled cloud and web engineering approach. The proposed system integrates secure cloud infrastructure, role-based data access, and privacy-preserving analytics to ensure compliance with healthcare regulations while enabling precise audience segmentation. LLMs are leveraged to analyze structured and unstructured ERP data, generate context-aware advertising insights, and optimize campaign strategies in real time. Advanced digital marketing analytics further enhance decision-making by measuring engagement, conversion performance, and campaign effectiveness. The framework demonstrates how intelligent automation and scalable cloud architecture can improve advertising relevance, operational efficiency, and trust within healthcare ERP ecosystems.

References

1. Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, SE-13(2), 222–232.

2. Anuj Arora, “Analyzing Best Practices and Strategies for Encrypting Data at Rest (Stored) and Data in Transit (Transmitted) in Cloud Environments”, “INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING”, VOL. 6 ISSUE 4 ( OCTOBER- DECEMBER 2018).

3. Lippmann, R., Haines, J. W., Fried, D. J., Korba, J., & Das, K. (2000). The 1999 DARPA off-line intrusion detection evaluation. Computer Networks, 34(4), 579–595.

4. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235–255.

5. Axelsson, S. (2000). Intrusion detection systems: A survey and taxonomy. Technical Report, Chalmers University of Technology.

6. Jaikrishna, G., & Rajendran, S. (2020). Cost-effective privacy preserving of intermediate data using group search optimisation algorithm. International Journal of Business Information Systems, 35(2), 132-151.

7. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 15:1–15:58.

8. Vasugi, T. (2022). AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4319-4325.

9. Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.

10. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

11. Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10), 4915–4928.

12. Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3796.

13. Vijayaboopathy, V., & Dhanorkar, T. (2021). LLM-Powered Declarative Blueprint Synthesis for Enterprise Back-End Workflows. American Journal of Autonomous Systems and Robotics Engineering, 1, 617-655.

14. Pichaimani, T., Inampudi, R. K., & Ratnala, A. K. (2021). Generative AI for Optimizing Enterprise Search: Leveraging Deep Learning Models to Automate Knowledge Discovery and Employee Onboarding Processes. Journal of Artificial Intelligence Research, 1(2), 109-148.

15. Paul, D. et al., "Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach," Journal of Science & Technology, vol. 2, no. 3, Sept. 8, (2021). https://thesciencebrigade.com/jst/article/view/382

16. Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602–613.

17. Yousefi-Azar, N., Kaghazgaran, P., & Sharafoddini, A. (2019). A comprehensive survey on machine learning techniques in credit card fraud detection. International Journal of Advanced Computer Science and Applications.

18. Lichman, M. (2013). UCI Machine Learning Repository. University of California, Irvine.

19. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. Proceedings of ICLR.

20. Akoglu, L., Tong, H., & Koutra, D. (2015). Graph-based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 29(3), 626–688.

21. Meka, S. (2022). Streamlining Financial Operations: Developing Multi-Interface Contract Transfer Systems for Efficiency and Security. International Journal of Computer Technology and Electronics Communication, 5(2), 4821-4829.

22. 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.

23. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.

24. Navandar, P. (2021). Fortifying cybersecurity in Healthcare ERP systems: unveiling challenges, proposing solutions, and envisioning future perspectives. Int J Sci Res, 10(5), 1322-1325.

25. Hardial Singh, “ENHANCING CLOUD SECURITY POSTURE WITH AI-DRIVEN THREAT DETECTION AND RESPONSE MECHANISMS”, INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), VOLUME-6, ISSUE-2, 2019.

26. Jeetha Lakshmi, P. S., Saravan Kumar, S., & Suresh, A. (2014). Intelligent Medical Diagnosis System Using Weighted Genetic and New Weighted Fuzzy C-Means Clustering Algorithm. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 213-220). New Delhi: Springer India.

27. Zuech, R., Khoshgoftaar, T. M., & Wald, R. (2015). Intrusion detection and Big Heterogeneous Data: A survey. Journal of Big Data, 2, 3.