Àngela Nebot

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Félix Castro

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Publications

2012
[j15]
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  - journals/ijcisys/NebotMCE12
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Àngela Nebot, Francisco Mugica, Félix Castro, Jesús Antonio Acosta Sarmiento:
Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling. Int. J. Comput. Intell. Syst. 5(2): 387-402 (2012)
[c27]
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  - conf/simultech/NebotMC12
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Àngela Nebot, Francisco Mugica, Félix Castro:
A Framework to Provide Real Time Useful Knowledge in E-Learning Environments. SIMULTECH 2012: 103-108
2011
[j13]
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  - journals/asc/CastroNM11
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Félix Castro, Àngela Nebot, Francisco Mugica:
On the extraction of decision support rules from fuzzy predictive models. Appl. Soft Comput. 11(4): 3463-3475 (2011)
2010
[c20]
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  - conf/fuzzIEEE/NebotMCA10
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Àngela Nebot, Francisco Mugica, Félix Castro, Jesús Antonio Acosta Sarmiento:
Genetic fuzzy system for predictive and decision support modelling in e-learning. FUZZ-IEEE 2010: 1-8
[c19]
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  - conf/ijcnn/NebotMC10
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Àngela Nebot, Francisco Mugica, Félix Castro:
Fuzzy predictive models to help teachers in e-learning courses. IJCNN 2010: 1-7
2009
[j10]
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  - journals/ijgs/NebotMC09
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Àngela Nebot, Francisco Mugica, Félix Castro:
Causal relevance to improve the prediction accuracy of dynamical systems using inductive reasoning. Int. J. Gen. Syst. 38(3): 331-358 (2009)
2008
[c17]
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  - conf/kes/NebotCVJA08
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Àngela Nebot, Félix Castro, Alfredo Vellido, Margarida Julià-Sapé, Carles Arús:
Rule-Based Assistance to Brain Tumour Diagnosis Using LR-FIR. KES (2) 2008: 173-180
[c16]
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  - conf/springsim/CastroNM08
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Félix Castro, Àngela Nebot, Francisco Mugica:
A soft computing decision support framework to improve the e-learning experience. SpringSim 2008: 781-788
2005
[c8]
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  - conf/fecs/CastroVNM05
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Félix Castro, Alfredo Vellido, Àngela Nebot, Julià Minguillón:
Finding Relevant Features to Characterize Student Behavior on an e-Learning System. FECS 2005: 210-

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