Examinando por Materia "machine learning"
Mostrando 1 - 8 de 8
Resultados por página
Opciones de ordenación
Ítem ¿Cómo serán las ciudades en el futuro? Esta herramienta lo pronostica(Universidad EAFIT, 2020-12-01) Martinez Guerrero, Christian Alexander; Martinez-Guerrero, Christian Alexander; Gómez Escobar, Jairo Alejandro; Patiño, Jorge E.; Duque, Juan C; Passos, Patiño Santiago; Research in Spatial EconomicsÍtem Este modelo permite tomar decisiones tempranas y acertadas ante el crecimiento poblacional(2021-04-05) Martinez Guerrero, Christian Alexander; Christian Alexander Martinez-Guerrero; Gomez, Jairo; Patiño, Jorge; Duque, Juan; Passos, Santiago; Vicerrectoría de Descubrimiento y CreaciónÍtem Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery(MDPI AG, 2017-09-01) Duque JC; Patiño, Jorge; Betancourt, Alejandro; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)Slum identification in urban settlements is a crucial step in the process of formulation of pro-poor policies. However, the use of conventional methods for slum detection such as field surveys can be time-consuming and costly. This paper explores the possibility of implementing a low-cost standardized method for slum detection. We use spectral, texture and structural features extracted from very high spatial resolution imagery as input data and evaluate the capability of three machine learning algorithms (Logistic Regression, Support Vector Machine and Random Forest) to classify urban areas as slum or no-slum. Using data from Buenos Aires (Argentina), Medellin (Colombia) and Recife (Brazil), we found that Support Vector Machine with radial basis kernel delivers the best performance (with F2-scores over 0.81). We also found that singularities within cities preclude the use of a unified classification model.Ítem Inteligencia artificial para detectar la roya en el café(Universidad EAFIT, 2020-12-01) Martinez Guerrero, Christian Alexander; Martinez-Guerrero, Christian Alexander; Velásquez, David; Sánchez, Alejandro; Sarmiento, Sebastian; Toro, Mauricio; Maiza, Mikel; Sierra, Basilio; GIDITIC; Estudios en MantenimientoÍtem The possibilities of artificial intelligence development(UNIV COOPERATIVE COLOMBIA, 2019-01-01) Quintero-Montoya, OL; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoIn the context of the broad positioning of artificial intelligence-thanks to the effects of globalization generated by large computer companies called glasses: Google, Amazon, Facebook and Apple-it is imperative to take up the fundamental theoretical aspects, the variety of technical aspects, the relevance of applications in different areas and the ethical implications surrounding intelligent systems. In the framework of this book, this chapter aims to demystify the aspects that have idealized machine learning and artificial intelligence. At the same time, it invites to discuss the elements that allow the global and national context to be key to next developments that will increase the economic capacity of the countries, the research advances and the level of life of the human beings.Ítem Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning(Public Library of Science, 2017-05-02) Arribas-Bel D; Patino JE; Duque JC; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively. © 2017 Arribas-Bel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Ítem Si te gusta el café, tienes que ver esto(2020-12-01) Martinez Guerrero, Christian Alexander; Christian Alexander Martinez-Guerrero; Velásquez, David; Sánchez, Alejandro; Sarmiento, Sebastián; Toro, Mauricio; Maiza, Mikel; Sierra, Basilio; Estudios en Mantenimiento; GIDITICÍtem ¿Te gusta un buen café? Este hongo lo puede destruir(2020-12-01) Martinez Guerrero, Christian Alexander; Christian Alexander Martinez-Guerrero; Vasquez, David; Sanchez, Alejandro; Sarmiento, Sebastian; Toro, Mauricio; Maiza, Mikel; Sierra, Basilio; Vicerrectoría de Descubrimiento y Creación