International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Integrating AI-Driven Analytics into Instrumentation Systems Using Enterprise Architecture Principles |
|---|---|
| ABSTRACT | The rapid evolution of Artificial Intelligence (AI) and advanced analytics has significantly transformed traditional instrumentation systems used in industrial, healthcare, agricultural, and public-sector engineering applications. Conventional instrumentation systems, while reliable, often operate in isolation, lacking advanced intelligence, scalability, and adaptability. Integrating AI-driven analytics into instrumentation systems offers unprecedented opportunities for real-time decision-making, predictive maintenance, operational efficiency, and system optimization. However, such integration introduces architectural challenges related to scalability, interoperability, security, and lifecycle management. This paper proposes an enterprise architecture–driven approach for integrating AI-driven analytics into modern instrumentation systems. By leveraging layered architecture, cloud-native principles, domain-driven design, and API-based integration, the proposed approach enables scalable, secure, and maintainable intelligent instrumentation solutions. The study examines architectural patterns, data pipelines, AI integration strategies, and governance considerations while addressing challenges such as technical debt, system complexity, and compliance requirements. The paper concludes with a discussion on future trends and the role of enterprise architecture in sustaining intelligent instrumentation ecosystems. |
| AUTHOR | MADHU BABU AMARAPPALLI Enterprise Solution Architect, Department of Artificial Intelligence & Engineering, Deloitte Consulting LLC, Mechanicsburg, Pennsylvania, USA |
| VOLUME | 168 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1304314 |
| pdf/314_Integrating AI-Driven Analytics into Instrumentation Systems Using Enterprise Architecture Principles.pdf | |
| KEYWORDS | |
| References | 1. Russell, S., & Norvig, P., Artificial Intelligence: A Modern Approach, Pearson, 2021. 2. Lankhorst, M., Enterprise Architecture at Work, Springer, 2017. 3. Doebelin, E. O., Measurement Systems: Application and Design, McGraw-Hill, 2018. 4. Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2016. 5. C. C. Marella and A. Palakurti, “Harnessing Python for AI and machine learning: Techniques, tools, and green solutions,” In Advances in Environmental Engineering and Green Technologies, IGI Global, 2025, pp. 237–250. 6. Palakurti, A., & Kodi, D. (2025). “Building intelligent systems with Python: An AI and ML journey for social good”. In Advancing social equity through accessible green innovation (pp. 1–16). IGI Global.. 7. Bass, L., Clements, P., & Kazman, R., Software Architecture in Practice, Addison-Wesley, 2021. 8. Shi, W. et al., “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, 2016. 9. Kleppmann, M., Designing Data-Intensive Applications, O’Reilly, 2017. 10. Sculley, D. et al., “Hidden Technical Debt in Machine Learning Systems,” NIPS, 2015. 11. Fielding, R., “Architectural Styles and the Design of Network-based Software Architectures,” 2000. 12. Few, S., Information Dashboard Design, Analytics Press, 2013. 13. ISO/IEC 27001, Information Security Management Systems, 2022. 14. Armbrust, M. et al., “A View of Cloud Computing,” Communications of the ACM, 2010. 15. Naga Ramesh Palakurti, Leveraging AI and Deep Learning for Risk Management in Agricultural Farms: Optimizing Maintenance with Data-Driven Insights, 2024 16. Kreutzer, J. et al., “Machine Learning Operations (MLOps),” IEEE Software, 2020. 17. Lee, J., Bagheri, B., & Kao, H., “A Cyber-Physical Systems Architecture for Industry 4.0,” Manufacturing Letters, 2015. 18. Topol, E., Deep Medicine, Basic Books, 2019. 19. Wolfert, S. et al., “Big Data in Smart Farming,” Agricultural Systems, 2017. 20. Floridi, L. et al., “AI Ethics Guidelines,” Nature Machine Intelligence, 2018. 21. Grieves, M., Digital Twin: Manufacturing Excellence, 2020. |
