Miguel Jesus Torres-Ruiz

PhD in Computer Science, Professor at Geospatial Information Processing Laboratory, in Centro de Investigación en Computación, Mexico City. Interested on geospatial information, ontologies and data maps. Member of the Mexican researchers syndicate.

Publications

Classification of Traffic Events in Mexico City Using Machine Learning and Volunteered Geographic Information
Magdalena Saldana-Perez, Miguel Torres-Ruiz, Marco Moreno-Ibarra. © 2022. 21 pages.
Volunteer geographic information and user-generated content represents a source of updated information about what people perceive from their environment. Its analysis generates...
GIS Approach for Collaborative Monitoring and Prediction of Environmental Noise in Urban Areas
Juan H. Juarez, Marco A. Moreno, Miguel J. Torres-Ruiz. © 2019. 27 pages.
Environmental noise, as well as being a form of environmental pollution that affects mainly urban areas, it is a problem that involves people's quality of life. This paper...
Classification of Traffic Events in Mexico City Using Machine Learning and Volunteered Geographic Information
Magdalena Saldana-Perez, Miguel Torres-Ruiz, Marco Moreno-Ibarra. © 2019. 22 pages.
Volunteer geographic information and user-generated content represents a source of updated information about what people perceive from their environment. Its analysis generates...
Classification of Traffic Events Notified in Social Networks' Texts
Ana Maria Magdalena Saldana-Perez, Marco Antonio Moreno-Ibarra, Miguel Jesus Torres-Ruiz. © 2019. 14 pages.
It is interesting to exploit the user-generated content (UGC) and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from UGC...
Classification of Traffic Events Notified in Social Networks' Texts
Ana Maria Magdalena Saldana-Perez, Marco Antonio Moreno-Ibarra, Miguel Jesus Torres-Ruiz. © 2018. 12 pages.
It is interesting to exploit the user generated content (UGC), and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from...
Innovation on User-Generated Content for Environmental Noise Monitoring and Analysis in the Context of Smart Cities
Juan Humberto Juárez Hipólito, Marco Antonio Moreno Ibarra, Miguel Torres-Ruiz, Giovanni Guzmán, Rolando Quintero. © 2018. 30 pages.
This work presents an approach based on the concept of Volunteered Geographic Information (VGI) to monitoring environmental noise; it is a problem that specially affects...
Innovation on Geo-Enrichment of Texts Using Gazetteers for Massive Open On-Line Courses
Vladimir Luna-Soto, Rolando Quintero, Miguel Torres-Ruiz, Marco Moreno-Ibarra, Imelda Escamilla. © 2018. 13 pages.
Text documents available in the Web contain a large amount of geographic information. For instance, forum messages posted by students in Massive Open Online Courses (MOOCs) may...
Geocoding Tweets Approach Based on Conceptual Representations in the Context of the Knowledge Society
Imelda Escamilla, Miguel Torres-Ruiz, Marco Moreno-Ibarra, Rolando Quintero, Giovanni Guzmán, Vladimir Luna-Soto. © 2016. 18 pages.
In this paper, an approach to geocode tweets published in Spanish is proposed. The tweets are related to traffic events within an urban context of the Mexico City. They are...
GIS Approach for Collaborative Monitoring and Prediction of Environmental Noise in Urban areas
Juan H. Juarez, Marco A. Moreno, Miguel J. Torres-Ruiz. © 2016. 25 pages.
Environmental noise, as well as being a form of environmental pollution that affects mainly urban areas, it is a problem that involves people's quality of life. This paper...
Urban Computing and Smart Cities Applications for the Knowledge Society
Miguel J. Torres-Ruiz, Miltiadis D. Lytras. © 2016. 7 pages.
During the last years, we faced a tremendous development of mobile sensing applications powered by innovative technologies related to ubiquitous and pervasive computing...
Applying Supervised Clustering to Landsat MSS Images into GIS-Application
Miguel Torres, Marco Moreno-Ibarra, Rolando Quintero, Giovanni Guzmán. © 2013. 9 pages.
In this paper, the authors describe and implement an algorithm to perform a supervised classification into Landsat MSS satellite images. The Maximum Likelihood Classification...