Abstract
This paper presents the effective use of a machine learning-based approach in predicting the resonance of slotted ground plane (SGP) resonators. The resonance prediction model was developed employing a convolution neural network (CNN) and images. In particular, a synthetic dataset based on the surface current distribution plots of designed SGP resonators. As a result, the trained CNN-based model demonstrated the 94% accuracy in the resonant frequency prediction. Finally, it is imperative to note that the resonance values for simulation, experimental, and prediction are in good consonance.