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@article{203397,
        author = {Ms.Molly Mohite and Shweta yande},
        title = {Automated and Manual Synthetic Data Generation for Machine Learning Training},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12185-12187},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203397},
        abstract = {This research work provides an in-depth analysis of the accuracy of synthetically generated datasets. The research provides a comprehensive coverage of the application, approaches and basic requirements of the AI-based automated tools in the domain of Machine Learning (ML) with the primary aim of enhancing the ability of the machines to work with human-like intelligence and responsiveness. This research also explores the application of synthetic data generation techniques as a vital component of enhancing the efficacy of model training and alleviating the issues of data scarcity. By the end, readers will have a profound appreciation of the importance and uses of synthetic data, presented in a structured, step-by-step fashion that showcases its crucial contribution to the progress of AI systems .},
        keywords = {Synthetic data Generation, Data augmentation, Deep Generative Models ,Tabular GAN},
        month = {May},
        }