Prof. Dr. Miguel Torres-Ruiz
Computer Science and PIIG Lab, Instituto Politécnico Nacional, Mexico City, Ciudad de México, Mexico
Professor and Researcher at the Instituto Politécnico Nacion...
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Academic Career Length
22Years (2005-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications8 September 2022 in Journal of Innovation & Knowledge
Journal Article
Miltiadis D. Lytras
Andreea Claudia SerbanMiguel Torres-Ruiz
Stamatios NtanosASAkila Sarirete
Akila Sarirete
The COVID-19 pandemic represented a significant challenge to higher education and forced academic institutions worldwide to abruptly shift to remote teaching. Higher education institutions are continuously confronted with the difficulties of creating satisfactory online learning experiences that adhere to the new norms. This paper presents a study concerning the perceptions of the quality and efficiency of the educational process in higher education concerning distance learning at the Instituto Politécnico Nacional of Mexico during the COVID-19 pandemic. The data analysis identified the level of satisfaction with distance learning education separately for students and professors and the lived experience for future education models. Data analysis consisted of a stepwise regression model to identify the most important variables related to the satisfaction aspect of e-learning for students and professors. The main findings are associated with the perception of one's own skills as the most crucial variable to define the aspects of e-learning satisfaction. Therefore, these skills involve the knowledge of e-learning platforms and the adoption rate of new technologies. The study found that the students emphasized that the variable "low sense of community" negatively impacts e-learning due to the loss of personal contact with the academic workspace. The key contribution of our research is a knowledge translation into a sustainable innovation capability model.
29 September 2018 in Virtual Reality
Journal Article
Miguel Torres-Ruiz
Miguel Felix Mata
Roberto Zagal FloresGGGiovanni Guzmán-Lugo
Giovanni Guzmán-Lugo
Rolando QuinteroMMMarco Antonio Moreno-Ibarra
Marco Antonio Moreno-IbarraJournal Article
JSJuan Salazar-Carrillo
Juan Salazar-CarrilloMiguel Torres-RuizCDClodoveu Davis
Clodoveu DavisRolando QuinteroMMMarco Antonio Moreno-Ibarra
Marco Antonio Moreno-IbarraGGGiovanni Guzmán-Lugo
Giovanni Guzmán-LugoSmart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic-related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web-GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.
Journal Article
Kwok Tai Chui
Brij Gupta
Hao Ran ChiVAVarsha Arya
Varsha AryaWAWadee ALHALABI
Wadee ALHALABICSChien-wen Shen
Chien-wen Shen +1
Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.
1 January 2016 in Mobile Information Systems
Journal Article
FMFélix Mata
Félix Mata
Miguel Torres-RuizGGGiovanni Guzmán-Lugo
Giovanni Guzmán-LugoRolando QuinteroRoberto Zagal FloresELEduardo Loza
Eduardo Loza +1
Mobile information systems agendas are increasingly becoming an essential part of human life and they play an important role in several daily activities. These have been developed for different contexts such as public facilities in smart cities, health care, traffic congestions, e-commerce, financial security, user-generated content, and crowdsourcing. In GIScience, problems related to routing systems have been deeply explored by using several techniques, but they are not focused on security or crime rates. In this paper, an approach to provide estimations defined by crime rates for generating safe routes in mobile devices is proposed. It consists of integrating crowd-sensed and official crime data with a mobile application. Thus, data are semantically processed by an ontology and classified by the Bayes algorithm. A geospatial repository was used to store tweets related to crime events of Mexico City and official reports that were geocoded for obtaining safe routes. A forecast related to crime events that can occur in a certain place with the collected information was performed. The novelty is a hybrid approach based on semantic processing to retrieve relevant data from unstructured data sources and a classifier algorithm to collect relevant crime data from official government reports with a mobile application.
1 October 2015 in Computers in Human Behavior
Journal Article
VLVladimir Luna
Vladimir Luna
Rolando QuinteroMiguel Torres-RuizMMMarco Antonio Moreno-Ibarra
Marco Antonio Moreno-IbarraGGGiovanni Guzmán-Lugo
Giovanni Guzmán-LugoIEImelda Escamilla
Imelda Escamilla
About Me
Prof. Torres-Ruiz received his PhD and Master’s in Computer Science from the National Polytechnic Institute. In 2002, he completed a research fellowship at the National Center for Geography of the Russian Academy of Sciences. In 2013, he completed the research fellowship at the University of California, Santa Cruz. He is a member of the National System of Researchers from the Mexican Government. Currently, he is a full professor at the Computing Research Center, IPN, particularly in the Intelligent Geospatial Information Processing Laboratory. Prof. Torres-Ruiz received the Research Award from the National Polytechnic Institute in 2008 and the Banamex Award. In 2016, he received an honorable mention in the National Civil Protection Award, granted by the Mexican President. He received the "Best Graduate Thesis in Cartography, Geodesy, and Geographic Information" award in 2017, granted by the Pan American Institute of Geography and History of the Organization of American States for his supervision of a graduate thesis. Prof. Torres-Ruiz is a member of various scientific associations and serves as an associate and guest editor for several international journals. His research focuses on urban computing, smart cities, and artificial intelligence, focusing on geographic information science and healthcare. He has published approximately 200 scientific articles in different journals and conferences and has supervised 80 theses at the undergraduate, master's, and doctoral levels.
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