Generative AI Models for Synthetic Data Creation to Enhance Machine Learning Performance
Abstract
This paper presents a generative AI framework designed to produce high-quality synthetic datasets for improving machine learning model training. The approach integrates variational autoencoders and adversarial learning to replicate complex data distributions while preserving statistical fidelity. Experiments conducted on image, text, and tabular datasets demonstrate improved model robustness, reduced overfitting, and enhanced performance, particularly in scenarios with limited real-world data. The study highlights synthetic data generation as a viable solution for scalable and privacy-preserving AI applications.
References
GANGADHARARAMACHARY RAMADUGU. (2023). CLOUD-NATIVE DIGITAL TRANSFORMATION: LESSONS FROM LARGE-SCALE DATA MIGRATIONS. International Journal of Innovation Studies, 7(1). 41-54
GANGADHARARAMACHARY RAMADUGU. (2024). SPRING BOOT 3 AND JAVA 21: ADVANCING MODERN APPLICATION DEVELOPMENT FOR FINANCIAL SERVICES. International Journal of Innovation Studies, 8(2), 556–564.
Ramadugu, G. (2024). Scaling Software Development Teams: Best Practices for Managing Cross-Functional Teams in Global Software Projects. International Journal of Communication Networks and Information Security (IJCNIS), 14(3), 766–775
Anup Rao. (2023). Real-Time Management and Analytics of High-Throughput IOT Device Data in Cloud Using Microsoft Teams Devices. International Journal of Computational and Experimental Science and Engineering, 9(4).
Russell, S., & Norvig, P. (2018). Advances in intelligent agent design for complex decision systems. International Journal of Artificial Intelligence Research, 12(3), 145–162.
Li, X., & Zhao, H. (2017). Deep learning approaches for large-scale pattern classification. Journal of Machine Intelligence, 9(2), 88–104.
Kumar, R., & Singh, A. (2016). Evolutionary optimization techniques for autonomous robot navigation. International Journal of Computational Vision and Robotics, 5(4), 201–214.
Chen, Y., & Huang, M. (2015). Neural architectures for natural language understanding. Journal of Intelligent Information Processing, 8(1), 33–49.
Patel, D., & Mehta, S. (2014). Hybrid machine learning methods for predictive analytics in healthcare. International Journal of Biomedical Computing, 21(2), 71–85.
Gupta, V., & Sharma, P. (2013). Computational models for adaptive learning in intelligent tutoring systems. Journal of Educational Technology and AI, 7(3), 112–128.
Ahmed, F., & Rahman, M. (2012). Fuzzy logic–based decision models for real-time control systems. International Journal of Soft Computing and Engineering, 4(1), 19–27.
Wang, L., & Kim, S. (2011). Reinforcement learning strategies for multi-agent coordination. Journal of Autonomous Intelligent Systems, 6(2), 54–70.
Rao, A. (2023). Design Patterns for Context-Aware Conversational Agents in Enterprise Systems. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 931–936.
Rao, A. (2024, December 9). Enhancing Enterprise Azure Application Delivery: Optimizing CI/CD Pipeline Performance and Continuous Iteration with Azure Devops Monitoring. Computer Fraud and Security. 10, 20-25.
Rao, A. (2024). Data Caching Strategies In High-Volume Applications Using Azure Redis And Serverless Computing. Journal of Informatics Education and Research, 2(3).
Rao, A. (2024). ACTIVE MONITORING AND AUTOMATED RECOVERY SYSTEMS USING AI AGENTS AND CONTINUOUS DATA PROCESSING. International Development Planning Review, 23(2), 2387–2395.
Pathik Bavadiya. (2023). Microservice-Aware CI/CD Pipelines: Dependency Graphs, Build Isolation, And Deployment Orchestration. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 677 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7854
Pathik Bavadiya. (2023). Security-As-Code: Integrating Automated Security Policies into Devops Pipelines. Journal of Informatics Education and Research, 3(2), 3103–3109. https://jier.org/index.php/journal/article/view/3583/2854
Pathik Bavadiya. (2024). Ansible Upgrade in Mission-Critical Systems: Ensuring Backward Compatibility and Role Integrity. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 709–715. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7855
Bavadiya, P. (2024). SONARQUBE-DRIVEN QUALITY GATES: IMPROVING SOFTWARE INTEGRITY THROUGH AUTOMATED CODE REVIEWS. International Journal of Applied Engineering & Technology, 6(2), 100–106. Retrieved from https://romanpub.com/resources/ijaet-v6-2-2024-10.pdf
Ramadugu, G. (2021). Continuous Integration and Delivery in Cloud-Native Environments: Best Practices for Large-Scale Saas Migrations. International Journal of Communication Networks and Information Security (IJCNIS), 13(1), 246–254.
Ramadugu, G. (2021). Digital Banking: A Blueprint for Modernizing Legacy Systems. International Journal on Recent and Innovation Trends in Computing and Communication; Auricle Global Society of Education and Research. 9(10), 47-52
Rao, A. (2020). A FAULT-TOLERANT MICROSERVICE FRAMEWORK LEVERAGING AZURE FUNCTIONS AND DISTRIBUTED REDIS CACHING. International Journal of Communication Networks and Information Security, 2020(3).
Rao, A. (2021). ARCHITECTURAL TRADE-OFFS BETWEEN STATELESS AND STATEFUL MICROSERVICES IN LARGE-SCALE CLOUD SYSTEMS. International Journal of Innovation Studies, 5(1), 135–141
