Enhancing Network Analysis Through Computational Intelligence in GANs

Abstract

In the discipline of allowsrative artificial intelligence, generative adversarial networks have become an effective tool that allow for the creation, modification, and synthesis of extremely realistic content in a variety of domains. This chapter focuses on applying computational intelligence techniques to improve network analysis in GANs. The authors examine the research on GANs' uses in radiology, emphasizing their potential for diagnosis and image enhancement in healthcare. Next, we investigate the application of computational intelligence techniques, like Wasserstein GANs and recurrent neural networks, to enhance training stability and produce higher-quality generated data. In order to increase the accuracy of the generated data even further, they also look into adding other features made with the Fourier transform and ARIMA. Trials show that the information produced by these upgraded GANs can be efficiently used for training energy forecasting models.