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Member since October, 2016
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I am a System Engineer (Magna Cum Laude), MSc in Statistics and PhD in Applied Sciences (Engineering), with experience in industrial, commercial, Government and academic environments. Since 2002, I am a Professor, Researcher and Thesis Tutor in the following fields: Data Mining, Machine Learning, Artificial Intelligence, Statistics, Optimization, Operations Research, Automatic Speech Processing and Computing. Also, I have 24 years of experience in software developing and IT Support.
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Associate Professor

Sep 2002

My job consists in teaching, researching and thesis tutoring in the following fields: Data Mining, Machine Learning, Artificial Intelligence, Statistics, Optimization, Operations Research, Systems Engineering, Automatic Speech Recognition and Computing.

Software Developer and Consultant

Feb 1999 - Feb 2001 (2 years)

- Software Development (Visual Basic, SQL Server, Access, Crystal Reports) - Consulting - Customization/Consulting of ERP Systems (Epicor Software Corporation, Customization Workbench)

IT Staff

Apr 1997 - Jul 1997 (3 months)

- Software Development (Visual Basic, Access, SQL) - Web Development - User Support (Windows)

Teaching Assistant

May 1995 - Oct 1998 (3 years)

- Teaching (Mathematics) - Software Development (Visual Basic, Access, SQL, Unix Shell) - User Support (Unix / Sun Microsystems) - Web Development


PhD in Applied Sciences and Engineering

2010 - 2016 (6 years)

MSc in Statistics

2003 - 2007 (4 years)

Bachelor's Degree in Systems Engineering (Specialized in Operations Research)

1992 - 1998 (6 years)


Prediction of financial time series with Time-Line Hidden Markov Experts and ANNs

In this paper, the use of Time-Line Hidden Markov Experts (THME) in the prediction of financial time series is presented and its efficiency is compared with that obtained using multilayer perceptron neural networks trained with BKP. Experiments were carried out with 15 series of financial time series in which most of the world’s bursatile indexes can be found. The results show that THME models greatly surpass those of Artificial Neural Networks.

Redes Neuronales Artificiales a Partir de la Función de Supervivencia de Kaplan-Meier

In this research, we estimate the Kaplan-Meier survival function using an artificial neural network (ANNs). The results demonstrate that the models of artificial neural networks allow the managing of survival data without needing to impose departure assumptions in the mentioned models. Thus, it is evident the potential of the ANNs to evaluate the partial information from a censured data set of survival.

A Hybrid HMM/ANN Method for Stock Index Forecasting

In this paper we present an hybrid method that combines Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) to forecast Stock Indexes. The aim of this research was to compare the predictions of the hybrid method (for stock indexes) with those generated by pure ANNs. The experiments were carried out with 15 stock indexes. The results show that the hybrid method greatly surpass those of ANNs, not only for its precision but its ability to detect patterns.

A Sandstones Classification Model

In this paper, a deterministic discriminant model to classify sandstones belonging to the clastic sedimentary rocks, is formulated. This model represents the triangular diagrams of the Pettijohn, Potter and Siever’s classification method, which is the most used in the clastic sedimentary petrology studies, because of its treatment of the component elements. The results show that the model classifies without error all types of sandstones, with a very simple computational code.

Off-line Signature Recognition Using Support Vector Machines

This paper presents two approaches for static signature recognition using Support Vector Machines (SVM): pure SVM and SVM integrated with a multilayer perceptron Artificial Neural Network (SVM/ANN) to map the results of the SVM. A practical advantage of SVM/ANN architecture was decreasing the error of confusing the actual signer with another one: when the model misclassified a signer, instead of classifying it as a wrong signer, the proposed architecture recognized it as unknown.

Scholarly Gratitude in Five Geographical Contexts in Medical Discourse (1950–2010)

This study analyzed the use of acknowledgements in medical articles published in five countries (Venezuela, Spain, France, UK and USA) from 1950 to 2010. For each country, we selected 54 papers (18 research papers, 18 reviews and 18 case reports), evenly distributed over six decades. We conclude that the concept of intellectual indebtedness does not only differ from one geographical context to another, but also over time and from one academic genre to another.

A hybrid system based on HMM and SVM for phone recognition in venezuelan continuous speech

The performance of an automatic speech recognizer based on Hidden Markov Models (HMMs) and Support Vector Machines (SVMs) is here compared to the performance of two other recognizers: one based on HMMs only (the HMM recognizer), and the other is a hybrid model based on HMMs and SVMs (the HMM/SVM recognizer). The recognition tests performed showed a significantly better performance of the hybrid recognizers when compared to that of the recognizers based on the HMMs only.

Comparison of Venezuela´s minimum wage purchasing power with that in other four countries in 2008

This paper compares Venezuela´s minimum wage purchasing power with that in Argentina, Colombia, Chile and Peru, using data mining. Using Bayesian networks it was found that the consumption pattern for the 108 goods considered is similar in these countries. The order of countries according to their minimum wage purchasing power from highest to lowest was found to be Argentina, Chile, Colombia, Venezuela and Peru.

Analysis of the Efficiency of Weighted Median-based Signal Reconstruction Through Cox Regression

In this paper, the efficiency of the algorithm for compressive sensing (CS) signal reconstruction based on weighted median regression (WMR) is analyzed through a Cox-regression model. We perform 1620 reconstructions for signals with different dimension (N), sparsity (K), number of measurements (M) and regularization parameter (α) that induces sparsity in the solution.

Automatic phoneme recognition in Venezuelan continuous speech based on HMMs and ANNs hybrid systems

In this paper we propose and test two hybrid approaches based on hidden Markov models (HMMs) and artificial neural networks (ANNs) for automatic speech recognition. The performance of these hybrid approaches is compared to the performance of a recognizer based on HMMs only (the HMM recognizer).

La Función de Supervivencia y las Redes Neuronales Artificiales, Caso CVG-Venalum (Book)

This is a book (available in ) where it's described how to estimate the Kaplan-Meier survival function using artificial neural networks (ANNs).

Analysis of the factor that influence the training of an HMM through the Cox Regression

Analysis of the factor that influence the training of an HMM through the Cox Regression


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