M
Márcio das Chagas Moura
Researcher at Federal University of Pernambuco
Publications - 72
Citations - 952
Márcio das Chagas Moura is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Computer science & Reliability (statistics). The author has an hindex of 12, co-authored 59 publications receiving 630 citations.
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Failure and reliability prediction by support vector machines regression of time series data
TL;DR: A comparative analysis of SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.
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Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis
Gabriel San Martin,Enrique López Droguett,Enrique López Droguett,Viviane Meruane,Márcio das Chagas Moura +4 more
TL;DR: The results show that variational auto-encoders are a competent and promising tool for dimensionality reduction for use in fault diagnosis and worth further exploring their capabilities beyond vibration signals of ball bearing elements.
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Real-time classification for autonomous drowsiness detection using eye aspect ratio
Caio Bezerra Souto Maior,Márcio das Chagas Moura,João Mateus Marques De Santana,Isis Didier Lins +3 more
TL;DR: A methodology for drowsiness detection based on eye patterns of people monitored by video streams using a low-cost real-time system to detect whether a user (operator) is drowsy using a simple web camera is developed.
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A particle swarm‐optimized support vector machine for reliability prediction
Isis Didier Lins,Márcio das Chagas Moura,Enrico Zio,Enrico Zio,Enrico Zio,Enrique López Droguett +5 more
TL;DR: Comparisons of the obtained results with those given by other time series techniques indicate that the PSO + SVM model is able to provide reliability predictions with comparable or great accuracy.
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Prediction of sea surface temperature in the tropical Atlantic by support vector machines
TL;DR: A year-ahead prediction procedure based on SST knowledge of previous periods is proposed and coupled with Support Vector Machines (SVMs), focused on seasonal and intraseasonal aspects of SST.