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Módulo de parametrización y prueba de algoritmos para aprendizaje de máquinas en línea/ fuera de línea para el análisis de trayectoria GPS

Abstract:

This paper presents the development and evaluation of a module for the parameterization and testing of online/offline machine learning algorithms for GPS trajectory analysis. The main objectives of this research are to develop a set of tools that allow users to experiment with different machine learning algorithms and evaluate their performance in analyzing GPS trajectories, as well as to provide an adaptable module that suits the specific needs of the user. The theoretical framework of the project focuses on machine learning algorithms and their applications in the analysis of GPS trajectories. The module has been developed using a combination of the Python programming language and open-source libraries, and allows customization of parameters and settings to suit the specific needs of users. The importance of this work lies in the increasing relevance of GPS technology in many fields, such as transportation, logistics, and the need for accurate and efficient GPS trajectory analysis. This work can contribute to the development of more accurate and efficient GPS trajectory analysis tools, and serve as a solid foundation for future research in this field. The methodology used in this work includes the development and implementation of the module, the testing and evaluation of different machine learning algorithms using real-world GPS track data, and the analysis of the impact of parameter settings on the performance of these algorithms. The results of the experiments show that the module is capable of significantly improving the performance of GPS trajectory analysis. In conclusion, the development and evaluation of this module can contribute to the improvement of GPS technology and its applications in various fields. The customizable and adaptable nature of the module allows for a wide range of applications, and its successful implementation and evaluation using real-world data provide a solid foundation for further research and development in this field.