Título :	Red Neuronal Recurrente de Memoria a Largo y Corto Plazo Optimizada por Procesos Gaussianos para Predecir la Contaminación Atmosférica
Autor(es):	Marco Antonio Olguin Sánchez
Palabras clave:	Ingeniería y Tecnología Ciencias Tecnológicas Ciencia de los ordenadores
Fecha de publicación :	22-abr-2024
Editorial :	Ingeniería
Facultad:	Facultad de Ingeniería
Programa académico:	Ingeniería Física
Resumen:	Forecasting air pollution is a challenging problem today that requires special attention in large cities since they are home to millions of people who are at risk of respiratory diseases every day. At the same time, there has been exponential growth in the research and application of deep learning, which is useful to treat temporary data such as pollution levels, leaving aside the physical and chemical characteristics of the particles and only focusing on predicting the next levels of contamination. This work seeks to contribute to society by presenting a useful way to optimize recurrent neural networks of the short and long-term memory type through a statistical process (Gaussian processes) for the correct optimization of the processes.
URI:	https://ri-ng.uaq.mx/handle/123456789/8905
Aparece en:	Ingeniería Física

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