G
Gonzalo A. Ruz
Researcher at Adolfo Ibáñez University
Publications - 81
Citations - 1123
Gonzalo A. Ruz is an academic researcher from Adolfo Ibáñez University. The author has contributed to research in topics: Computer science & Boolean network. The author has an hindex of 15, co-authored 64 publications receiving 766 citations. Previous affiliations of Gonzalo A. Ruz include Coordenadoria de Aperfeiçoamento de Pessoal de Nível Superior & Cardiff University.
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Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers
TL;DR: This work considers Bayesian network classifiers to perform sentiment analysis on two datasets in Spanish: the 2010 Chilean earthquake and the 2017 Catalan independence referendum, and adopts a Bayes factor approach, yielding more realistic networks.
A Medical Claim Fraud/Abuse Detection System based on Data Mining: A Case Study in Chile.
TL;DR: The application of data mining to a real industrial problem through the implementation of an automatic fraud detection system changed the original non-standard medical claims checking process to a standardized process helping to fight against new, unusual and known fraudulent/abusive behaviors.
Journal Article
A neurofuzzy color image segmentation method for wood surface defect detection
TL;DR: In this paper, a neurofuzzy color image segmentation method for wood surface defect detection is proposed, which grows boxes from a set of pixels called seeds, to find the minimum bounded rectangle (MBR) for each defect present in the wood board image.
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Automated visual inspection system for wood defect classification using computational intelligence techniques
TL;DR: Improvements in the segmentation module, feature extraction module, and the classification module of a low-cost automated visual inspection (AVI) system for wood defect classification are presented and the use of computational intelligence techniques improved significantly the overall performance of the proposed automated visual inspectors for wood boards.
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Job performance prediction in a call center using a naive Bayes classifier
TL;DR: It is suggested that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the agent.