J. M. Barrón-Adame
Universidad de Guanajuato
22 Papers
91 Citations
J. M. Barrón-Adame is an academic researcher from Universidad de Guanajuato. The author has contributed to research in topics: Artificial neural network & Cluster analysis. The author has an hindex of 7, co-authored 20 publications.
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Papers
Prevision of industrial SO 2 pollutant concentration applying ANNs
M. G. Cortina-Januchs,J. M. Barrón-Adame,A. Vega-Corona,Diego Andina +3 more
- 23 Jun 2009
TL;DR: In this paper, the authors applied feed forward artificial neural network to predict the air pollution concentrations in Salamanca, Mexico, focusing on the daily maximum concentration of SO 2, which is one of the most important environmental problems.
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Automatic Detection of Microcalcifications in ROI Images Based on PFCM and ANN
J. Quintanilla-Dominguez,B. Ojeda-Magaña,Alexis Marcano-Cedeño,J. M. Barrón-Adame,A. Vega-Corona,Diego Andina +5 more
- 04 Dec 2013
TL;DR: An artificial neural network model is used to learn the relations between atypical pixels and microcalcifications, such that the model can be used for aid diagnosis, and a medical could determine if these regions of interest are benign or malignant.
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Air Pollutant Level Estimation Applying a Self-organizing Neural Network
J. M. Barrón-Adame,J. A. Herrera Delgado,M. G. Cortina-Januchs,Diego Andina,A. Vega-Corona +4 more
- 18 Jun 2007
TL;DR: A real time series from an Automatic Environmental Monitoring Network from Salamanca, Guanajuato, Mexico is considered, and therefore in this proposal a real Air Pollutant Level is also estimated.
10
Image Segmentation Using Ant System-Based Clustering Algorithm
Aleksandar Jevtic,J. Quintanilla-Dominguez,J. M. Barrón-Adame,Diego Andina +3 more
- 01 Jan 2011
TL;DR: An image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA), which models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path, and is applied to detection of microcalcifications in digital mammograms.
Contextual and Non-Contextual Features Extraction and a Selection Method for Microcalcifications Detection
A. Vega-Corona,M. Sanchez-Garcia,M. Gonzalez-Romo,J. Quintanilla-Dominguez,J. M. Barrón-Adame +4 more
- 24 Jul 2006
TL;DR: The proposed method consist in a combination of two steps, in the first one, a feature extraction method is applied using multiscale wavelet image processing, and is combined with a Self Organizing Neural Network to solve the segmentation image problem.
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