Papers by NOEL GARCIA DIAZ
Revista Iberoamericana de las Ciencias Sociales y Humanísticas, Feb 23, 2016
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Revista Iberoamericana de las Ciencias Sociales y Humanísticas: RICSH, 2016
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3C TIC, Sep 29, 2017
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RIIIT. Revista internacional de investigación e innovación tecnológica, Jun 1, 2020
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Journal of Information Systems Engineering & Management, 2016
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International Journal of Engineering Education, 2016
Prediction techniques have been applied for predicting dependent variables related to Higher Educ... more Prediction techniques have been applied for predicting dependent variables related to Higher Education students such asdropout, grades, course selection, and satisfaction. In this research, we propose a prediction technique for predicting theeffort of software projects individually developed by graduate students. In accordance with the complexity of a softwareproject, it can be developed among teams, by a team or even at individual level. The teaching and training aboutdevelopment effort prediction of software projects represents a concern in environments related to academy and industrybecause underprediction causes cost overruns, whereas overprediction often involves missed financial opportunities.Effort prediction techniques of individually developed projects have mainly been based on expert judgment or based onmathematical models. This research proposes the application of a mathematical model termed Radial Basis functionNeural Network (RBFNN). The hypothesis to be tested is the f...
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IEEE Latin America Transactions
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2015 10th Iberian Conference on Information Systems and Technologies (CISTI), 2015
Two of the three most important causes of Information Technology projects failure have been relat... more Two of the three most important causes of Information Technology projects failure have been related to a poor resource estimation. In average, software developers expend from 30% to 40% more effort than is estimated. Because that no single technique to estimate software development effort is best for all situations, it is important to propose new models to compare their results and then generate more realistic estimates. In this study, the aimed is to present a hybrid model that combine fuzzy logic and neural networks for achieving higher accuracy for estimating the development time of software projects. The accuracy of time estimation for a Neuro-Fuzzy System (NFS) is statistically better than the accuracy obtained from a previous NFS and statistical regression (model most used by default to compare) when the forty-one modules developed from ten programs were used as dataset. Results show that the value of MMRE (Mean of Magnitude of Relative Error) applying a NFS was substantially lower than MMRE applying a previous NFS and statistical regression. It can be conclude that a new NFS could be applied for estimating the effort of software development projects when they have been individually developed on a disciplined process.
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Procedia Technology, 2013
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IEEE Latin American Transactions, 2021
Research on student performance prediction has evolved from the early application of statistical ... more Research on student performance prediction has evolved from the early application of statistical techniques to later use of computational techniques. Results in this field are varied, thus, we have to take advantage of previous research results. This study proposes a Multi-layer Adaptive Neuro-Fuzzy Inference System (MANFIS) for student performance prediction in online Higher Education settings. The MANFIS was trained and tested using a dataset integrated by the scores obtained by students in four online Higher Education courses. The MANFIS prediction accuracy was compared against the accuracies of Multilayer neural network, Radial Basis Function Neural Network, and General Regression Neural Network. The accuracy of the MANFIS prediction statistically outperformed at least one neural network (out of three possible) in each dataset. The Results indicate that MANFIS is an alternative model to predict student performance in online Higher Education settings.
Index Terms-Computational Intelligence, Fuzzy Systems, Hybrid Intelligent Systems, Neural Networks, Multi-layer Adaptive Fuzzy Inference System, student performance prediction, online education, e-learning
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Prediction techniques have been applied for predicting dependent variables related to Higher Educ... more Prediction techniques have been applied for predicting dependent variables related to Higher Education students such as
dropout, grades, course selection, and satisfaction. In this research, we propose a prediction technique for predicting the
effort of software projects individually developed by graduate students. In accordance with the complexity of a software
project, it can be developed among teams, by a team or even at individual level. The teaching and training about
development effort prediction of software projects represents a concern in environments related to academy and industry
because underprediction causes cost overruns, whereas overprediction often involves missed financial opportunities.
Effort prediction techniques of individually developed projects have mainly been based on expert judgment or based on
mathematical models. This research proposes the application of a mathematical model termed Radial Basis function
Neural Network (RBFNN). The hypothesis to be tested is the following: effort prediction accuracy of a RBFNN is
statistically better than that obtained from a Multiple Linear Regression (MLR). The projects were developed by following
a disciplined development process in controlled environments. The RBFNNandMLRwere trained from a data set of 328
projects developed by 82 students between the years 2005 and 2010, then, the models were tested using a data set of 116
projects developed by 29 students between the years 2011 and first semester of 2012. Results suggest that aRBFNNhaving
as independent variables new and changed code, reused code and programming language experience of students can be
used at the 95.0% confidence level for predicting the development effort of individual projects when they have been
developed based upon a disciplined process in academic environments.
Bookmarks Related papers MentionsView impact
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Papers by NOEL GARCIA DIAZ
Index Terms-Computational Intelligence, Fuzzy Systems, Hybrid Intelligent Systems, Neural Networks, Multi-layer Adaptive Fuzzy Inference System, student performance prediction, online education, e-learning
dropout, grades, course selection, and satisfaction. In this research, we propose a prediction technique for predicting the
effort of software projects individually developed by graduate students. In accordance with the complexity of a software
project, it can be developed among teams, by a team or even at individual level. The teaching and training about
development effort prediction of software projects represents a concern in environments related to academy and industry
because underprediction causes cost overruns, whereas overprediction often involves missed financial opportunities.
Effort prediction techniques of individually developed projects have mainly been based on expert judgment or based on
mathematical models. This research proposes the application of a mathematical model termed Radial Basis function
Neural Network (RBFNN). The hypothesis to be tested is the following: effort prediction accuracy of a RBFNN is
statistically better than that obtained from a Multiple Linear Regression (MLR). The projects were developed by following
a disciplined development process in controlled environments. The RBFNNandMLRwere trained from a data set of 328
projects developed by 82 students between the years 2005 and 2010, then, the models were tested using a data set of 116
projects developed by 29 students between the years 2011 and first semester of 2012. Results suggest that aRBFNNhaving
as independent variables new and changed code, reused code and programming language experience of students can be
used at the 95.0% confidence level for predicting the development effort of individual projects when they have been
developed based upon a disciplined process in academic environments.
Index Terms-Computational Intelligence, Fuzzy Systems, Hybrid Intelligent Systems, Neural Networks, Multi-layer Adaptive Fuzzy Inference System, student performance prediction, online education, e-learning
dropout, grades, course selection, and satisfaction. In this research, we propose a prediction technique for predicting the
effort of software projects individually developed by graduate students. In accordance with the complexity of a software
project, it can be developed among teams, by a team or even at individual level. The teaching and training about
development effort prediction of software projects represents a concern in environments related to academy and industry
because underprediction causes cost overruns, whereas overprediction often involves missed financial opportunities.
Effort prediction techniques of individually developed projects have mainly been based on expert judgment or based on
mathematical models. This research proposes the application of a mathematical model termed Radial Basis function
Neural Network (RBFNN). The hypothesis to be tested is the following: effort prediction accuracy of a RBFNN is
statistically better than that obtained from a Multiple Linear Regression (MLR). The projects were developed by following
a disciplined development process in controlled environments. The RBFNNandMLRwere trained from a data set of 328
projects developed by 82 students between the years 2005 and 2010, then, the models were tested using a data set of 116
projects developed by 29 students between the years 2011 and first semester of 2012. Results suggest that aRBFNNhaving
as independent variables new and changed code, reused code and programming language experience of students can be
used at the 95.0% confidence level for predicting the development effort of individual projects when they have been
developed based upon a disciplined process in academic environments.