Circuits and graphs recognition using Artificial Intelligence and Machine Learning techniques.

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) techniques has paved the way for innovative applications in various domains. This thesis explores the intersection of AI and ML in the realms of circuit and graph recognition. The objective is to develop intelligent systems capable of automatically identifying and understanding circuits and graphs, reducing human intervention in these complex tasks. The proposed approach leverages advanced deep learning models and neural networks to analyze and interpret visual representations of circuits and graphs. Image recognition algorithms are employed to identify components, connections, and patterns within the visual data, while graph-based models aid in understanding the underlying structures. The training of these models involves a diverse dataset, encompassing a wide range of circuit configurations and graph types.