International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 984KB)
Synthetic Data Generation Using Generative Adversarial Networks for Enterprise Application
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Author(s)
Index Terms
Generative Adversarial Network, Machine Learning, One-shot Learning, Python, Synthetic Data Generation, Enterprise Applications
Abstract
This research tackles the fundamental requirement of synthetic data generation to tighten up machine learning model precision and one-shot shot learning to lessen the need to pursue data input. The project aims to develop a service that can generate reasonable synthetic data from a given dataset. It was first designed and developed, and then the project structure was set, and libraries were chosen for predevelopment analysis. This continued development process also included subsequent phases that included dataset collecting, assessment, and iterative research. Different hyperparameters were run over multiple models to select an optimal configuration. To evaluate the model's performance over produced synthetic datasets, about 1.5 and 2 times the original, synthetic data was produced, providing a basis for a robust synthetic data generating process.
Cite This Paper
Sreevishnu A. B., Suman De, "Synthetic Data Generation Using Generative Adversarial Networks for Enterprise Application", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.6, pp. 71-80, 2025. DOI:10.5815/ijieeb.2025.06.06
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