Cloud-Native AI Model for Real-Time Project Risk Prediction Using Transaction Analysis and Caching Strategies
Main Article Content
Abstract
Real-time project environments demand intelligent systems capable of accurately predicting risks, maintaining transactional stability, and ensuring high system performance. This paper introduces a cloud-native AI model that integrates transaction analysis with optimized caching strategies to enhance predictive accuracy and operational responsiveness. The proposed architecture leverages machine learning techniques to analyze transactional patterns, detect anomalies, and forecast emerging risk factors across distributed project workflows. Multi-layer caching mechanisms are employed to reduce data retrieval latency, improve throughput, and support continuous model training and inference. The system operates within a scalable cloud ecosystem, enabling dynamic resource allocation and high-availability monitoring services. Experimental results demonstrate that combining AI-driven transaction analytics with caching-based performance optimization significantly improves real-time risk detection, minimizes bottlenecks, and supports proactive decision-making within complex project management environments.
Article Details
Section
How to Cite
References
1. Flyvbjerg, B. (2013). From Nobel Prize to Project Management: Getting Risks Right. SSRN. arXiv
2. Kendrick, T. (2003). Identifying and Managing Project Risk. American Management Association. Wikipedia
3. Nagarajan, G. (2022). An integrated cloud and network-aware AI architecture for optimizing project prioritization in healthcare strategic portfolios. International Journal of Research and Applied Innovations, 5(1), 6444–6450. https://doi.org/10.15662/IJRAI.2022.0501004
4. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
5. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
6. Virine, L., & Trumper, M. (2007). Event chain methodology. In Project Decisions: The Art and Science (Berrett-Koehler Publishers). Wikipedia
7. Peram, S. (2022). Behavior-Based Ransomware Detection Using Multi-Layer Perceptron Neural Networks A Machine Learning Approach For Real-Time Threat Analysis. https://www.researchgate.net/profile/Sudhakara-Peram/publication/396293337_Behavior-Based_Ransomware_Detection_Using_Multi-Layer_Perceptron_Neural_Networks_A_Machine_Learning_Approach_For_Real-Time_Threat_Analysis/links/68e5f1bef3032e2b4be76f4a/Behavior-Based-Ransomware-Detection-Using-Multi-Layer-Perceptron-Neural-Networks-A-Machine-Learning-Approach-For-Real-Time-Threat-Analysis.pdf
8. Peddamukkula, P. K. How Technology is Making Life Insurance Smarter and Faster: The Role of Cloud and Automation. https://www.researchgate.net/profile/Praveen-Peddamukkula/publication/397017728_How_Technology_is_Making_Life_Insurance_Smarter_and_Faster_The_Role_of_Cloud_and_Automation/links/69023a0cc900be105cbd89d5/How-Technology-is-Making-Life-Insurance-Smarter-and-Faster-The-Role-of-Cloud-and-Automation.pdf
9. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
10. Karanjkar, R. (2022). Resiliency Testing in Cloud Infrastructure for Distributed Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7142-7144.
11. Kotapati, V. B. R., Perumalsamy, J., & Yakkanti, B. (2022). Risk-Adapted Investment Strategies using Quantum-enhanced Machine Learning Models. American Journal of Autonomous Systems and Robotics Engineering, 2, 279-312.
12. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
13. Kumar, S. N. P. (2022). Improving Fraud Detection in Credit Card Transactions Using Autoencoders and Deep Neural Networks (Doctoral dissertation, The George Washington University).
14. Mohile, A. (2021). Performance Optimization in Global Content Delivery Networks using Intelligent Caching and Routing Algorithms. International Journal of Research and Applied Innovations, 4(2), 4904-4912.
15. Dam, H. K., Tran, T., Grundy, J., Ghose, A., & Kamei, Y. (2018). Towards effective AI-powered agile project management. arXiv. arXiv
16. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
17. CloudTweaks. (2011). History Of Cloud Computing: A Journey Of Innovation And Future Prospects. (Discusses cloud’s early development in 2000s.)
18. Zerine, Ismoth, Md Mainul Islam, Md Saiful Islam, Md Yousuf Ahmad, and Md Arifur Rahman. "CLIMATE RISK ANALYTICS FOR US AGRICULTURE SUSTAINABILITY: MODELING CLIMATE IMPACT ON CROP YIELDS AND SUPPLY CHAIN TO SUPPORT FEDERAL POLICIES FOOD SECURITY AND RENEWABLE ANERGY ADOPTION." Cuestiones de Fisioterapia 49, no. 3 (2020): 241-258.
19. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
20. De Marco, A. (2022). Artificial Intelligence for Risk Management (PhD thesis). Politecnico di Torino. webthesis.biblio.polito.it
21. Ravipudi, S., Thangavelu, K., & Ramalingam, S. (2021). Automating Enterprise Security: Integrating DevSecOps into CI/CD Pipelines. American Journal of Data Science and Artificial Intelligence Innovations, 1, 31-68.
22. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
23. KM, Z., Akhtaruzzaman, K., & Tanvir Rahman, A. (2022). BUILDING TRUST IN AUTONOMOUS CYBER DECISION INFRASTRUCTURE THROUGH EXPLAINABLE AI. International Journal of Economy and Innovation, 29, 405-428.
24. Nair, R., & Meenakumari, J. (2022). IT Project Risk Management for Cloud Environment Leveraging Artificial Intelligence. International Journal of Research - Granthaalayah, 10(12), 55–68. Granthaalayah Publication+1
25. Dam, H. K., Tran, T., Grundy, J., Ghose, A., & Kamei, Y. (2018). Towards effective AI-powered agile project management. arXiv.