ALGORITMOS DE APRENDIZAJE COMPUTACIONAL PARA LA SEGMENTACIÓN Y CLASIFICACIÓN DE ANOMALÍAS EN VÍAS DE TRÁNSITO

ARAGÓN SAENZPARDO, MARIO EZRA (2017) ALGORITMOS DE APRENDIZAJE COMPUTACIONAL PARA LA SEGMENTACIÓN Y CLASIFICACIÓN DE ANOMALÍAS EN VÍAS DE TRÁNSITO. Maestría thesis, UNIVERSIDAD AUTONOMA DE CHIHUAHUA.

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Resumen

This work proposes a solution to a problem that exist nowadays, which is the presence of potholes in roads and highways, as well as the use of bumps and metal bumps that affect the comfort and safety of drivers. For this, it will be used the analysis of the conditions of the roads using different sensors of mobile devices to store and analyze the data, and be able to make a correct detection and classification of anomalies. It is suggested the use of machine learning techniques for the segmentation of the signals that were generated, and once they are segmented classify them using a bag of words strategy for the representation of different types of anomalies.

Tipo de Documento: Tesis (Maestría)
Palabras Clave: Machine Learning, Roadways, mobile devices.
Clasificación temática: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Usuario Remitente: Admin Administrador del Respositorio
Depositado: 28 Nov 2017 18:12
Ultima Modificación: 28 Nov 2017 18:12
URI: http://repositorio.uach.mx/id/eprint/140

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Universidad Autonoma de Chihuahua