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Claudia Isaza
Researcher at University of Antioquia
Publications - 30
Citations - 413
Claudia Isaza is an academic researcher from University of Antioquia. The author has contributed to research in topics: Fuzzy clustering & Cluster analysis. The author has an hindex of 11, co-authored 25 publications receiving 333 citations.
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Journal ArticleDOI
Automatic recognition of anuran species based on syllable identification
TL;DR: An unsupervised methodology for anuran automatic identification is proposed, based on the use of a fuzzy classifier and Mel Frequency Cepstral Coefficients, that is able to detect species not presented in the training stage, although they belong to different populations.
Journal ArticleDOI
Software development effort prediction of industrial projects applying a general regression neural network
TL;DR: Results obtained from a variance analysis of accuracies of the models suggest that a GRNN could be an alternative for predicting development effort of software projects that have been developed in industrial environments.
Proceedings ArticleDOI
Artificial Neural Networks as an alternative to traditional fall detection methods
TL;DR: This method intends to improve fall detection accuracy, by avoiding the traditional threshold - based fall detection methods, and introducing ANN as a suitable option on this application.
Journal ArticleDOI
Automatic identification of rainfall in acoustic recordings
TL;DR: In this article, a method for automatic detection of rainfall in forests by using acoustic recordings is proposed based on the estimation of the mean value and signal to noise ratio of the power spectral density in the frequency band in which the sound of the raindrops falling over the vegetation layers of the forest is more prominent (i.e. 600-1200 Hz).
Journal ArticleDOI
A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines
TL;DR: A new criterion to validate functional states after recognition (LAMDA-FAR), based on the minimum and maximum distances among MDV was developed, and it was found that the LAMDA algorithm alone was unable to properly classify similar engine operating modes, while the Laming Algorithm Multivariable and Data Analysis algorithm showed 100% efficiency for both known and unknown operating modes.