AI-Enabled Data Engineering Pipelines for Smart Grid Fault Detection
Abstract
Research on smart grids has drawn attention due to the increasing reliance on multiple sources of generation, such as solar and wind farms, and the decentralization of decision-making due to the increase in distributed generators. The implementation of new technologies such as machine learning (ML), available within the framework of artificial intelligence (AI), promises to increase reliability and efficiency by anticipating events and acting before they occur. AI-enabled data engineering pipelines integrate data-processing procedures—such as quality assurance, governance, ingestion, transformation, and preparation—with AI modeling techniques addressing applications such as fault detection and predictive maintenance. These pipelines embed advanced data-processing techniques and tools into a reliable management framework. Smart grids monitor power distribution, detect faults, and feed data, including that from phasor measurement units (PMUs), supervisory control and data acquisition (SCADA), and weather-related sensors.
Data flowing through the pipelines is analyzed in real-time using a fixed library of ML fault-detection models. Fixed models, configured in a lawyer-and-create manner, allow some level of exploration during the training phase of the pipeline. Delays in the deployment of fixed models represent a serious concern for many businesses; the situation is more critical for companies with a mature AI practice—the latency is built into the model and processes association—and is nevertheless significant for business pipelines. Stream processing pipelines execute each processing step as soon as new data arrive for that step. However, model evaluation requires more than a supervised test data set; measures are needed to control the false-positive rate in a business pipeline and assess model robustness against rare events such as blackouts or smaller events such as terminal failures.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
13195-13209 |
Published |
December 9, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Nareddy Abhireddy (%2024). AI-Enabled Data Engineering Pipelines for Smart Grid Fault Detection. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13195-13209. https://doi.org/10.15662/IJSRAT.2024.0706007 |
References
[1] Davuluri, P. S. L. N. (2024). AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness. Metallurgical and Materials Engineering, 30(4), 996–1010. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1936
[2] Guntupalli, R. (2024). Enhancing Cloud Security with AI: A Deep Learning Approach to Identify and Prevent Cyberattacks in Multi-Tenant Environments. Available at SSRN 5329132.
[3] Anderson, T. (Ed.). (2008). The theory and practice of online learning (2nd ed.). AU Press.
[4] Singireddy, J. (2024). Ai-enhanced tax preparation and filing: Automating complex regulatory compliance. European Data Science Journal (EDSJ) p-ISSN, 3050-9572.
[5] Anikina, Z., & Yakimenko, O. (2015). EdTech and digital learning environments in higher education: Trends and outcomes. Procedia - Social and Behavioral Sciences, 206, 432–436.
[6] Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
[7] Baker, R. S. J. d. (2010). Data mining for education. In B. McGaw, E. Baker, & P. Peterson (Eds.), International encyclopedia of education (3rd ed., pp. 112–118). Elsevier.
[8] Paleti, S. (2024). Transforming Financial Risk Management with AI and Data Engineering in the Modern Banking Sector. American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X, 2(1).
[9] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
[10] Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
[11] Bichsel, J. (2013). The state of e-learning in higher education: An eye toward growth and increased access. EDUCAUSE Center for Applied Research.
[12] Sheelam, G. K., & Koppolu, H. K. R. (2024). From Transistors to Intelligence: Semiconductor Architectures Empowering Agentic AI in 5G and Beyond. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 4518-4537.
[13] Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16.
[14] Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
[15] Brinton, C. G., Rill, R., Ha, S., Chiang, M., Smith, R., & Ju, W. (2014). Individualization for education at scale: Improving online learning via automated student modeling. IEEE Transactions on Learning Technologies, 7(4), 347–363.
[16] Singireddy, S. (2024). The Integration of AI and Machine Learning in Transforming Underwriting and Risk Assessment Across Personal and Commercial Insurance Lines. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 3966-3991.
[17] Challa, K. (2024). Neural Networks in Inclusive Financial Systems: Generative AI for Bridging the Gap Between Technology and Socioeconomic Equity. MSW Management Journal, 34(2), 749-763.
[18] A Scalable Web Platform for AI-Augmented Software Deployment in Automotive Edge Devices via Cloud Services. (2024).
[19] Campos, P., Desmarais, M., & S. d. Baker, R. (2011). Mining constraint-based tutor data for personalization. Educational Data Mining, 3(1), 13–26.
[20] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
[21] Nandan, B. P., & Chitta, S. S. (2023). Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice, 29(4), 4555-4568.
[22] Christensen, C. M., Horn, M. B., & Johnson, C. W. (2008). Disrupting class: How disruptive innovation will change the way the world learns. McGraw-Hill.
[23] Singireddy, J. (2024). AI-Driven Payroll Systems: Ensuring Compliance and Reducing Human Error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[24] Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695.
[25] Challa, K. (2024). Artificial Intelligence and Generative Neural Systems: Creating Smarter Customer Support Models for Digital Financial Services. Journal of Computational Analysis & Applications, 33(8).
[26] Emerging Role of Agentic AI in Designing Autonomous Data Products for Retirement and Group Insurance Platforms. (2024). MSW Management Journal, 34(2), 1464-1474.
[27] Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue. In Proceedings of the 6th International Conference on Learning Analytics & Knowledge (pp. 89–98). ACM.
[28] Singireddy, S. (2024). Predictive Modeling for Auto Insurance Risk Assessment Using Machine Learning Algorithms. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN, 3050-9734.
[29] Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls
[30] Ellis, R. A., & Goodyear, P. (2010). Students’ experiences of e-learning in higher education: The ecology of sustainable innovation. Routledge.
[31] Sriram, H. K., Challa, S. R., Challa, K., & ADUSUPALLI, B. (2024). Strategic Financial Growth: Strengthening Investment Management, Secure Transactions, and Risk Protection in the Digital Era. Secure Transactions, and Risk Protection in the Digital Era (November 10, 2024).
[32] Nandan, B. P. (2024). Semiconductor Process Innovation: Leveraging Big Data for Real-Time Decision-Making. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4038-4053.
[33] Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.
[34] Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Deep Learning, and Explainable AI (July 26, 2024).
[35] Gobert, J. D., Baker, R. S., & Wixon, M. (2015). Operationalizing affective constructs in educational software. In Proceedings of EDM 2015 (pp. 1–8). International Educational Data Mining Society.
[36] Graesser, A. C., Conley, M., & Olney, A. (2012). Intelligent tutoring systems. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook (pp. 451–473). American Psychological Association.
[37] Guntupalli, R. (2023). AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection. Available at SSRN 5329158.
[38] Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.
[39] Griffin, P., McGaw, B., & Care, E. (Eds.). (2012). Assessment and teaching of 21st century skills. Springer.
[40] Halawa, S., Greene, D., & Mitchell, J. (2014). Dropout prediction in MOOCs using learner activity features. In Proceedings of the 2nd European MOOC Stakeholder Summit (pp. 58–65).
[41] Guntupalli, R. (2024). AI-Powered Infrastructure Management in Cloud Computing: Automating Security Compliance and Performance Monitoring. Available at SSRN 5329147.
[42] Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
[43] Vardhan Kumar Bandi, V. D. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13(1), 2127-2141.
[44] Nandan, B. P. (2024). Revolutionizing Semiconductor Chip Design through Generative AI and Reinforcement Learning: A Novel Approach to Mask Patterning and Resolution Enhancement. International Journal of Medical Toxicology and Legal Medicine, 27(5), 759-772.
[45] Ionita, A., & Dede, C. (2016). Personalized learning and learning analytics in higher education. Journal of Learning Analytics, 3(1), 1–12.
[46] Singireddy, S. (2024). Applying deep learning to mobile home and flood insurance risk evaluation. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
[47] Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (pp. 170–179). ACM.
[48] Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476–486
[49] Koper, R. (2014). Conditions for effective smart learning environments. Smart Learning Environments, 1(1), 1–17.
[50] Kushvanth Chowdary Nagabhyru. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898–5910. https://doi.org/10.53555/kuey.v29i4.10932.
[51] Kuh, G. D. (2009). The national survey of student engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 141, 5–20.
[52] Mashetty, S., Challa, S. R., ADUSUPALLI, B., Singireddy, J., & Paleti, S. (2024). Intelligent Technologies for Modern Financial Ecosystems: Transforming Housing Finance, Risk Management, and Advisory Services Through Advanced Analytics and Secure Cloud Solutions. Risk Management, and Advisory Services Through Advanced Analytics and Secure Cloud Solutions (December 12, 2024).
[53] Lahari Pandiri, "AI-Powered Fraud Detection Systems in Professional and Contractors Insurance Claims," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI 10.17148/IJIREEICE.2024.121206.
[54] Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.
[55] Singireddy, S. (2024). Leveraging Artificial Intelligence and Agentic AI Models for Personalized Risk Assessment and Policy Customization in the Modern Insurance Industry: A Case Study on Customer-Centric Service Innovations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 2532-2545.
[56] Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
[57] Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
[58] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[59] Milligan, C., & Littlejohn, A. (2017). Why study on a MOOC? The motives of students and professionals. International Review of Research in Open and Distributed Learning, 18(2), 92–102.
[60] Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442–450. https://doi.org/10.61841/turcomat.v15i2.15464.
[61] Challa, K. (2024). Enhancing credit risk assessment using AI and big data in modern finance. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[62] Nelson, K. J., Creagh, T. A., & Clarke, J. A. (2012). Engagement and retention in the first year: Strategies for support. Higher Education Research & Development, 31(2), 217–229.
[63] Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
[64] Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.
[65] Pardo, A., Mirriahi, N., Martinez-Maldonado, R., Jovanovic, J., Dawson, S., & Gašević, D. (2019). A systematic review of learning analytics dashboards for higher education. Educational Technology Research and Development, 67, 1323–1346.
[66] Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review. Educational Technology & Society, 17(4), 49–64.
[67] Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
[68] Picciano, A. G. (2012). The evolution of big data and learning analytics in education. Journal of Asynchronous Learning Networks, 16(3), 9–20.
[69] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
[70] Reigeluth, C. M., & Carr-Chellman, A. A. (Eds.). (2009). Instructional-design theories and models: Building a common knowledge base (Vol. 3). Routledge.
[71] Kaulwar, P. K. (2024). Agentic Tax Intelligence: Designing Autonomous AI Advisors for Real-Time Tax Consulting and Compliance. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 2757-2775.
[72] Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
[73] Rose, C. P., & Siemens, G. (2014). Shared tasks for learning analytics. Journal of Learning Analytics, 1(1), 1–5.
[74] Rongali, S. K. (2024). Federated and Generative AI Models for Secure, Cross-Institutional Healthcare Data Interoperability. Journal of Neonatal Surgery, 13(1), 1683-1694.
[75] Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144.
[76] Salomon, G., & Perkins, D. N. (1998). Individual and social aspects of learning. Review of Research in Education, 23, 1–24.
[77] Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security. [78] Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
[79] Simpson, O. (2012). Supporting students in online, open and distance learning (2nd ed.). Routledge.
[80] Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.
[81] Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
[2] Guntupalli, R. (2024). Enhancing Cloud Security with AI: A Deep Learning Approach to Identify and Prevent Cyberattacks in Multi-Tenant Environments. Available at SSRN 5329132.
[3] Anderson, T. (Ed.). (2008). The theory and practice of online learning (2nd ed.). AU Press.
[4] Singireddy, J. (2024). Ai-enhanced tax preparation and filing: Automating complex regulatory compliance. European Data Science Journal (EDSJ) p-ISSN, 3050-9572.
[5] Anikina, Z., & Yakimenko, O. (2015). EdTech and digital learning environments in higher education: Trends and outcomes. Procedia - Social and Behavioral Sciences, 206, 432–436.
[6] Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
[7] Baker, R. S. J. d. (2010). Data mining for education. In B. McGaw, E. Baker, & P. Peterson (Eds.), International encyclopedia of education (3rd ed., pp. 112–118). Elsevier.
[8] Paleti, S. (2024). Transforming Financial Risk Management with AI and Data Engineering in the Modern Banking Sector. American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X, 2(1).
[9] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
[10] Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
[11] Bichsel, J. (2013). The state of e-learning in higher education: An eye toward growth and increased access. EDUCAUSE Center for Applied Research.
[12] Sheelam, G. K., & Koppolu, H. K. R. (2024). From Transistors to Intelligence: Semiconductor Architectures Empowering Agentic AI in 5G and Beyond. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 4518-4537.
[13] Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16.
[14] Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
[15] Brinton, C. G., Rill, R., Ha, S., Chiang, M., Smith, R., & Ju, W. (2014). Individualization for education at scale: Improving online learning via automated student modeling. IEEE Transactions on Learning Technologies, 7(4), 347–363.
[16] Singireddy, S. (2024). The Integration of AI and Machine Learning in Transforming Underwriting and Risk Assessment Across Personal and Commercial Insurance Lines. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 3966-3991.
[17] Challa, K. (2024). Neural Networks in Inclusive Financial Systems: Generative AI for Bridging the Gap Between Technology and Socioeconomic Equity. MSW Management Journal, 34(2), 749-763.
[18] A Scalable Web Platform for AI-Augmented Software Deployment in Automotive Edge Devices via Cloud Services. (2024).
[19] Campos, P., Desmarais, M., & S. d. Baker, R. (2011). Mining constraint-based tutor data for personalization. Educational Data Mining, 3(1), 13–26.
[20] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
[21] Nandan, B. P., & Chitta, S. S. (2023). Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice, 29(4), 4555-4568.
[22] Christensen, C. M., Horn, M. B., & Johnson, C. W. (2008). Disrupting class: How disruptive innovation will change the way the world learns. McGraw-Hill.
[23] Singireddy, J. (2024). AI-Driven Payroll Systems: Ensuring Compliance and Reducing Human Error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[24] Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695.
[25] Challa, K. (2024). Artificial Intelligence and Generative Neural Systems: Creating Smarter Customer Support Models for Digital Financial Services. Journal of Computational Analysis & Applications, 33(8).
[26] Emerging Role of Agentic AI in Designing Autonomous Data Products for Retirement and Group Insurance Platforms. (2024). MSW Management Journal, 34(2), 1464-1474.
[27] Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue. In Proceedings of the 6th International Conference on Learning Analytics & Knowledge (pp. 89–98). ACM.
[28] Singireddy, S. (2024). Predictive Modeling for Auto Insurance Risk Assessment Using Machine Learning Algorithms. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN, 3050-9734.
[29] Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls
[30] Ellis, R. A., & Goodyear, P. (2010). Students’ experiences of e-learning in higher education: The ecology of sustainable innovation. Routledge.
[31] Sriram, H. K., Challa, S. R., Challa, K., & ADUSUPALLI, B. (2024). Strategic Financial Growth: Strengthening Investment Management, Secure Transactions, and Risk Protection in the Digital Era. Secure Transactions, and Risk Protection in the Digital Era (November 10, 2024).
[32] Nandan, B. P. (2024). Semiconductor Process Innovation: Leveraging Big Data for Real-Time Decision-Making. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4038-4053.
[33] Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.
[34] Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Deep Learning, and Explainable AI (July 26, 2024).
[35] Gobert, J. D., Baker, R. S., & Wixon, M. (2015). Operationalizing affective constructs in educational software. In Proceedings of EDM 2015 (pp. 1–8). International Educational Data Mining Society.
[36] Graesser, A. C., Conley, M., & Olney, A. (2012). Intelligent tutoring systems. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook (pp. 451–473). American Psychological Association.
[37] Guntupalli, R. (2023). AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection. Available at SSRN 5329158.
[38] Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.
[39] Griffin, P., McGaw, B., & Care, E. (Eds.). (2012). Assessment and teaching of 21st century skills. Springer.
[40] Halawa, S., Greene, D., & Mitchell, J. (2014). Dropout prediction in MOOCs using learner activity features. In Proceedings of the 2nd European MOOC Stakeholder Summit (pp. 58–65).
[41] Guntupalli, R. (2024). AI-Powered Infrastructure Management in Cloud Computing: Automating Security Compliance and Performance Monitoring. Available at SSRN 5329147.
[42] Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
[43] Vardhan Kumar Bandi, V. D. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13(1), 2127-2141.
[44] Nandan, B. P. (2024). Revolutionizing Semiconductor Chip Design through Generative AI and Reinforcement Learning: A Novel Approach to Mask Patterning and Resolution Enhancement. International Journal of Medical Toxicology and Legal Medicine, 27(5), 759-772.
[45] Ionita, A., & Dede, C. (2016). Personalized learning and learning analytics in higher education. Journal of Learning Analytics, 3(1), 1–12.
[46] Singireddy, S. (2024). Applying deep learning to mobile home and flood insurance risk evaluation. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
[47] Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (pp. 170–179). ACM.
[48] Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476–486
[49] Koper, R. (2014). Conditions for effective smart learning environments. Smart Learning Environments, 1(1), 1–17.
[50] Kushvanth Chowdary Nagabhyru. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898–5910. https://doi.org/10.53555/kuey.v29i4.10932.
[51] Kuh, G. D. (2009). The national survey of student engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 141, 5–20.
[52] Mashetty, S., Challa, S. R., ADUSUPALLI, B., Singireddy, J., & Paleti, S. (2024). Intelligent Technologies for Modern Financial Ecosystems: Transforming Housing Finance, Risk Management, and Advisory Services Through Advanced Analytics and Secure Cloud Solutions. Risk Management, and Advisory Services Through Advanced Analytics and Secure Cloud Solutions (December 12, 2024).
[53] Lahari Pandiri, "AI-Powered Fraud Detection Systems in Professional and Contractors Insurance Claims," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI 10.17148/IJIREEICE.2024.121206.
[54] Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.
[55] Singireddy, S. (2024). Leveraging Artificial Intelligence and Agentic AI Models for Personalized Risk Assessment and Policy Customization in the Modern Insurance Industry: A Case Study on Customer-Centric Service Innovations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 2532-2545.
[56] Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
[57] Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
[58] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[59] Milligan, C., & Littlejohn, A. (2017). Why study on a MOOC? The motives of students and professionals. International Review of Research in Open and Distributed Learning, 18(2), 92–102.
[60] Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442–450. https://doi.org/10.61841/turcomat.v15i2.15464.
[61] Challa, K. (2024). Enhancing credit risk assessment using AI and big data in modern finance. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[62] Nelson, K. J., Creagh, T. A., & Clarke, J. A. (2012). Engagement and retention in the first year: Strategies for support. Higher Education Research & Development, 31(2), 217–229.
[63] Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
[64] Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.
[65] Pardo, A., Mirriahi, N., Martinez-Maldonado, R., Jovanovic, J., Dawson, S., & Gašević, D. (2019). A systematic review of learning analytics dashboards for higher education. Educational Technology Research and Development, 67, 1323–1346.
[66] Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review. Educational Technology & Society, 17(4), 49–64.
[67] Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
[68] Picciano, A. G. (2012). The evolution of big data and learning analytics in education. Journal of Asynchronous Learning Networks, 16(3), 9–20.
[69] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
[70] Reigeluth, C. M., & Carr-Chellman, A. A. (Eds.). (2009). Instructional-design theories and models: Building a common knowledge base (Vol. 3). Routledge.
[71] Kaulwar, P. K. (2024). Agentic Tax Intelligence: Designing Autonomous AI Advisors for Real-Time Tax Consulting and Compliance. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 2757-2775.
[72] Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
[73] Rose, C. P., & Siemens, G. (2014). Shared tasks for learning analytics. Journal of Learning Analytics, 1(1), 1–5.
[74] Rongali, S. K. (2024). Federated and Generative AI Models for Secure, Cross-Institutional Healthcare Data Interoperability. Journal of Neonatal Surgery, 13(1), 1683-1694.
[75] Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144.
[76] Salomon, G., & Perkins, D. N. (1998). Individual and social aspects of learning. Review of Research in Education, 23, 1–24.
[77] Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security. [78] Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
[79] Simpson, O. (2012). Supporting students in online, open and distance learning (2nd ed.). Routledge.
[80] Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.
[81] Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.