| AI-Driven Cloud Resource Optimization Framework for Real-Time Allocation V Ramamoorthi Journal of Advanced Computing Systems 1 (1), 8-15, 2021 | 27 | 2021 |
| Multi-Objective Optimization Framework for Cloud Applications Using AI-Based Surrogate Models V Ramamoorthi Journal of Big-Data Analytics and Cloud Computing 6 (2), 23-32, 2021 | 25 | 2021 |
| A Hybrid UDE+ NN Approach for Dynamic Performance Modeling in Microservices V Ramamoorthi Sage Science Review of Educational Technology 3 (1), 73-86, 2020 | 24 | 2020 |
| Machine Learning Models for Anomaly Detection in Microservices V Ramamoorthi Quarterly Journal of Emerging Technologies and Innovations 5 (1), 41-56, 2020 | 24 | 2020 |
| Optimizing Cloud Load Forecasting with a CNN-BiLSTM Hybrid Model V Ramamoorthi International Journal of Intelligent Automation and Computing 5 (2), 79-91, 2022 | 13 | 2022 |
| Hybrid CNN-GRU Scheduler for Energy-Efficient Task Allocation in Cloud–Fog Computing V Ramamoorthi Journal of Advanced Computing Systems 2 (2), 1-9, 2022 | 13 | 2022 |
| Real-Time Adaptive Orchestration of AI Microservices in Dynamic Edge Computing V Ramamoorthi Journal of Advanced Computing Systems 3 (3), 1-9, 2023 | 11 | 2023 |
| AI-Driven Partitioning Framework for Migrating Monolithic Applications to Microservices V Ramamoorthi Journal of Computational Social Dynamics 8 (11), 63-72, 2023 | 9 | 2023 |
| AI-Enhanced Performance Optimization for Microservice-Based Systems V Ramamoorthi Journal of Advanced Computing Systems 4 (9), 1-7, 2024 | 6 | 2024 |
| Anomaly detection and automated mitigation for microservices security with AI V Ramamoorthi Applied Research in Artificial Intelligence and Cloud Computing 7 (6), 211-222, 2024 | 5 | 2024 |
| Deep Learning Models on Cloud Platforms V Ramamoorthi ISBN : 978-81-977811-4-8 | DOI: 10.5281/zenodo.13121320, 2024 | | 2024 |
