The project proposes an in-depth exploration of advanced machine learning techniques for the automated classification of stellar spectra, aiming to significantly reduce computation time compared to current methods used by Astro+. Beyond improving efficiency, the project envisions the development of models capable of directly determining stellar parameters through deep learning, eliminating the need for manual model fitting. This approach would represent a major step toward fully automated and more efficient spectroscopic analysis.

The project aims to identify the key factors influencing the perception of urban safety in selected neighborhoods of Alicante by analyzing elements such as lighting quality, graffiti, population density, and other relevant indicators. Through the use of artificial intelligence and video analysis tools, it seeks to create an objective, data-driven diagnostic that maps urban conditions affecting perceived safety.