AI-Powered Deep Learning for Cross-Domain Predictive Intelligence in MIS, Insurance, and Air Quality with Optimized QA
DOI:
https://doi.org/10.15662/IJARCST.2025.0806001Keywords:
Cross-Domain Intelligence, Deep Learning, Management Information Systems, Insurance Technology, Air Quality Monitoring, Predictive Analytics, Quality Assurance, Resource Allocation, Computational Efficiency, Sustainable Intelligent SystemsAbstract
The convergence of diverse digital ecosystems demands advanced methods for predictive intelligence that can operate seamlessly across multiple domains. This paper presents an AI-powered deep learning framework for cross-domain predictive intelligence in management information systems (MIS), insurance platforms, and air quality monitoring infrastructures. The proposed system integrates neural architectures for accurate forecasting, anomaly detection, and event classification, enabling stakeholders to make proactive, data-driven decisions. In the insurance domain, the framework enhances claims prediction, risk assessment, and fraud detection; in environmental systems, it provides real-time air quality forecasting to support public health interventions; and in MIS, it improves resource planning and performance optimization in cloud-native environments. A distinctive contribution of this work is the introduction of an optimized quality assurance (QA) allocation layer, which dynamically prioritizes testing and validation efforts based on workload criticality, risk levels, and performance requirements. Experimental evaluation across heterogeneous datasets demonstrates the framework’s scalability, robustness, and high predictive accuracy, highlighting its effectiveness in bridging multiple sectors. This research underscores the transformative potential of AI-driven cross-domain intelligence, paving the way for adaptive, secure, and quality-assured digital ecosystems.
References
1. Abdulkadir, U. I., & Fernando, A. (2024). A deep learning model for insurance claims predictions. Journal on Artificial Intelligence, 6(1), 71–83. Tech Science
2. Raju, L. H. V., & Sugumar, R. (2025, June). Improving jaccard and dice during cancerous skin segmentation with UNet approach compared to SegNet. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020271). AIP Publishing LLC.
3. Devaraju, Sudheer. "Multi-Modal Trust Architecture for AI-HR Systems: Analyzing Technical Determinants of User Acceptance in Enterprise-Scale People Analytics Platforms." IJFMR, DOI 10.
4. Komarina, G. B. ENABLING REAL-TIME BUSINESS INTELLIGENCE INSIGHTS VIA SAP BW/4HANA AND CLOUD BI INTEGRATION.
5. Bekkar, A., Hssina, B., Douzi, S., & Douzi, K. (2021). Air pollution prediction in smart city, deep learning approach. Journal of Big Data, 8, Article 161. SpringerOpen
6. Gayathri, M., Kavitha, V., & Jeyaraj, A. (2024). Forecasting air quality with deep learning. International Journal of Intelligent Systems and Applications in Engineering. IJISAE
7. Hettige, K. H., Ji, J., Xiang, S., Long, C., Cong, G., & Wang, J. (2024). AirPhyNet: Harnessing physics guided neural networks for air quality prediction. arXiv preprint arXiv:2402.03784. arXiv
8. Karanjkar, R., & Karanjkar, D. (2024). Optimizing Quality Assurance Resource Allocation in Multi Team Software Development Environments. International Journal of Technology, Management and Humanities, 10(04), 49-59.
9. Murad, A., Kraemer, F. A., Bach, K., & Taylor, G. (2021). Probabilistic deep learning to quantify uncertainty in air quality forecasting. arXiv preprint arXiv:2112.02622. arXiv
10. Peddamukkula, P. K. (2024). Immersive Customer Engagement_The Impact of AR and VR Technologies on Consumer Behavior and Brand Loyalty. International Journal of Computer Technology and Electronics Communication, 7(4), 9118-9127.
11. Ishtiaq, W., Zannat, A., Parvez, A. S., Hossain, M. A., Kanchan, M. H., & Tarek, M. M. (2025). CST-AFNet: A Dual Attention-based Deep Learning Framework for Intrusion Detection in IoT Networks. Array, 100501.
12. Balaji, P. C., & Sugumar, R. (2025, June). Multi-level thresholding of RGB images using Mayfly algorithm comparison with Bat algorithm. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020180). AIP Publishing LLC.
13. Shankar, L., & Arasu, K. (2023). Deep learning techniques for air quality prediction: A focus on PM2.5 and periodicity. Migration Letters, 20(S13), 468–484. Migration Letters
14. “Machine learning algorithms to forecast air quality: a survey.” (2023). Artificial Intelligence Review, 56, 10031–10066. SpringerLink
15. Pareek, C.S. (2025). AI-Driven Software Testing: A Deep Learning Perspective. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 13(1), 1-10. https://www.ijirmps.org/research-paper.php?id=231946
16. Sethupathy, U. K. A. (2024). Fraud Detection Mechanisms in Virtual Payment Systems. International Journal of Computer Technology and Electronics Communication, 7(2), 8504-8515.
17. Gandhi, S. T. (2024). Fusion of LiDAR and HDR Imaging in Autonomous Vehicles: A Multi-Modal Deep Learning Approach for Safer Navigation. International Journal of Humanities and Information Technology, 6(03), 6-18.
18. P. Chatterjee, “AI-Powered Payment Gateways : Accelerating Transactions and Fortifying Security in RealTime Financial Systems,” Int. J. Sci. Res. Sci. Technol., 2023.
19. Patel, K., Pilgar, C., & Thakare, S. B. Agile Hardware Development: A Cross-Industry Exploration for Faster Prototyping and Reduced Time-to-Market.
20. Reddy, B. T. K., & Sugumar, R. (2025, June). Effective forest fire detection by UAV image using Resnet 50 compared over Google Net. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020274). AIP Publishing LLC.
21. “A deep learning approach for prediction of air quality index in a metropolitan city.” (2021). Sustainable Cities and Society, 67, 102720. ScienceDirect
22. “A systematic survey of air quality prediction based on deep learning.” (2024). Alexandria Engineering Journal, 93, 128–141. ScienceDirect
