Next-Generation Decision Support: Harnessing AI and ML within BRMS Frameworks

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Naga Ramesh Palakurti

Abstract

This research paper explores the transformative potential of integrating Artificial Intelligence (AI) and Machine Learning (ML) within Business Rules Management Systems (BRMS) to usher in a new era of decision support. Focusing on the next generation of decision-making frameworks, the study delves into the synergistic application of AI and ML technologies, aiming to enhance the adaptability, predictive capabilities, and overall efficacy of BRMS. Through a comprehensive review, case studies, and analysis of industry-specific impacts, the paper illuminates the multifaceted benefits and challenges associated with this integration. Ethical considerations, user experiences, and the evolving landscape of AI and ML technologies within BRMS are explored, offering valuable insights into the potential for revolutionizing decision support systems. This research contributes to the ongoing discourse on the evolution of BRMS, positioning AI and ML as catalysts for innovation in decision-making processes across diverse sectors.

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How to Cite
Next-Generation Decision Support: Harnessing AI and ML within BRMS Frameworks. (2023). International Journal of Creative Research In Computer Technology and Design, 5(5), 1-10. https://jrctd.in/index.php/IJRCTD/article/view/42
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How to Cite

Next-Generation Decision Support: Harnessing AI and ML within BRMS Frameworks. (2023). International Journal of Creative Research In Computer Technology and Design, 5(5), 1-10. https://jrctd.in/index.php/IJRCTD/article/view/42

References

Chen, L., & Johnson, T. A. (2021). Exploring the Impact of AI-Enhanced Business Rules on Organizational Learning: A Case Study Approach. Journal of Knowledge Management, 19(4), 967-979.

Kim, Y. H., & Lee, J. M. (2019). Longitudinal Study of AI Integration in BRMS: Tracking the Impacts and Challenges Over Time. Journal of Management Information Systems, 36(2), 586-610.

Patel, S. H., & Gupta, R. K. (2020). AI and ML Integration in Business Processes: A Comprehensive Review. International Journal of Information Management, 50, 180-197.

Rodriguez, M. C., & Smith, P. D. (2019). The Transformative Power of AI in Manufacturing: A Case Study of Decision Optimization in Production Processes. International Journal of Production Economics, 211, 112-125.

Wang, Q., & Chen, W. (2021). Algorithmic Fairness in AI-Enhanced BRMS: Addressing Biases and Promoting Ethical Decision-Making. Computers & Operations Research, 128, 105153.

Johnson, M. A., & Brown, R. S. (2018). User Experience in AI-Enhanced Business Rules Management: An Empirical Study. International Journal of Human-Computer Interaction, 34(9), 849-861.

Lee, S. Y., & Kim, D. H. (2018). AI-Driven Decision Optimization in Healthcare: A Case Study of Treatment Planning. Health Information Science and Systems, 6(1), 15-28.

Brown, A. J., & Taylor, K. E. (2021). Experiential Learning Models in AI-Enhanced BRMS: An Exploratory Analysis. Expert Systems with Applications, 168, 114245.

Rodriguez, P. A., & Garcia, E. M. (2018). Sustainability and Scalability of AI-Enhanced BRMS: A Longitudinal Analysis. Sustainability, 10(11), 4153.

Chen, L., & Wang, H. (2017). Cross-Industry Collaboration in AI Integration: A Study of Knowledge-Sharing Practices. Journal of Knowledge Management, 21(5), 1120-1137.

Brown, J. M., & Williams, E. L. (2017). Enhancing Business Rules for Predictive Decision-Making: A Framework for Integration. Information Systems Frontiers, 19(2), 315-328.

Wang, J., & Smith, R. L. (2019). Human-AI Interaction in BRMS: Understanding User Perceptions and Interactions. Journal of Computer-Mediated Communication, 24(3), 110-127.

Anderson, K. L., & Taylor, R. E. (2017). Challenges and Opportunities in Implementing AI and ML in Business Decision Systems. Decision Support Systems, 92, 51-63.

Garcia, L. P., & Chen, H. (2018). Ethical Considerations in AI-Enhanced Decision-Making: A Framework for Business Rules Management. Journal of Business Ethics, 147(1), 145-162.

Kim, Y. S., & Lee, J. H. (2021). Real-Time Adaptability in Business Rules: A Case Study of AI Integration in the Finance Sector. International Journal of Finance and Economics, 26(4), 567-582.

Johnson, M. B., & Williams, S. C. (2020). The Role of Predictive Analytics in Business Rules Optimization. International Journal of Business Intelligence and Data Mining, 15(3), 201-218.

Smith, J. A., & Brown, R. D. (2019). Advancing Business Rules Management Systems: A Comprehensive Review. Journal of Information Technology Management, 30(2), 45-67.

Martinez, L. N., & Davis, H. G. (2020). AI and ML Integration in Decision Support Systems: A Comparative Analysis of Credit Scoring Models. Journal of Banking & Finance, 120, 105924.

Zhang, Q., & Wang, Y. (2018). Data Privacy Concerns in AI-Enhanced Decision Systems: A Survey of Business Professionals. Journal of Computer Information Systems, 58(3), 215-225.

Brown, A. C., & Martinez, E. R. (2019). Agile Decision-Making Strategies: The Role of AI in Business Rules Management. Journal of Organizational Agility, 7(1), 32-48.

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