Trends in computer intelligence
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computational intelligence, computational methods, bibliometric networks, intelligent systems
This article presents those results derived from a systematic review of the literature focused on the recognition of computational intelligence worldwide. The methodological development was constituted in four different facets: - the elaboration of search equations, - the verification of the quality and relevance of the documents, - the elaboration of schemes for the recognition of the trends, - the investigation of confluences and differences. 55 articles from the Scopus® and Web of Science® databases were examined in VOSviewer® software for the construction and visualization of bibliometric networks and term concurrence; resulting in machine learning, classification, artificial intelligence, feature selection, algorithm classification, neural networks, intelligent systems among others. This study yields results from various fields of evolutionary learning for data collection to provide new and diverse faster applications, allowing data optimization and new prediction strategies or tools.
K. Börner, C. Chen, y K. W. Boyack, “Visualizing knowledge domains”, Annu. Rev. Inf. Sci. Technol., vol. 37, pp. 179–255, 2003 DOI: https://doi.org/10.1002/aris.1440370106
C. Okoli y K. Schabram, “Guide to Conducting a Systematic Literature Review of Information Systems Research”, Work. Pap. Inf. Syst., 2010 DOI: https://doi.org/10.2139/ssrn.1954824
M. Dixon-Woods, S. Agarwal, D. Jones, B. Young, y A. Sutton, “Synthesising qualitative and quantitative evidence: a review of possible methods”, J. Heal. Serv. Res. Policy, vol. 10, núm. 1, pp. 45–53, ene. 2005 DOI: https://doi.org/10.1177/135581960501000110
A. J. Onwuegbuzie, N. L. Leech, y K. M. T. Collins, “Qualitative Analysis Techniques for the Review of the Literature”, 2012
M. J. Grant y A. Booth, “A typology of reviews: an analysis of 14 review types and associated methodologies”, Heal. Inf. Libr. J., vol. 26, núm. 2, pp. 91–108, jun. 2009 DOI: https://doi.org/10.1111/j.1471-1842.2009.00848.x
A. Christoforou y A. S. Andreou, “A Multilayer Fuzzy Cognitive Maps approach to the cloud adoption decision support problem”, en IEEE International Conference on Fuzzy Systems, 2015, vol. 2015-November, pp. 1–8 DOI: https://doi.org/10.1109/FUZZ-IEEE.2015.7338056
S. S. Ge, T. M. Guerra, F. L. Lewis, J. C. Principe, y M. Colnarič, “Computational Intelligence in Control”, IFAC Proc. Vol., vol. 47, núm. 3, pp. 8867–8878, 2014 DOI: https://doi.org/10.3182/20140824-6-ZA-1003.01164
S. Saeedi, R. Chavarriaga, R. Leeb, y J. D. R. Millan, “Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability”, IEEE Comput. Intell. Mag., vol. 11, núm. 1, pp. 32–39, feb. 2016 DOI: https://doi.org/10.1109/MCI.2015.2501550
L. Wang, Q. Zhang, A. Zhou, M. Gong, y L. Jiao, “Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm”, IEEE Trans. Evol. Comput., vol. 20, núm. 3, pp. 475–480, jun. 2016 DOI: https://doi.org/10.1109/TEVC.2015.2457616
V. Piuri, F. Scotti, y M. Roveri, “Computational intelligence in industrial quality control”, en IEEE International Workshop on Intelligent Signal Processing - Proceedings, 2005, pp. 4–9 DOI: https://doi.org/10.1109/WISP.2005.1531623
E. Lakshika y M. Barlow, “Computational Intelligence Approaches to Computational Aesthetics”, 2018, pp. 81–92 DOI: https://doi.org/10.4018/978-1-5225-7368-5.ch007
M. Frank, D. Drikakis, y V. Charissis, “Machine-learning methods for computational science and engineering”, Computation, vol. 8, núm. 1. MDPI Multidisciplinary Digital Publishing Institute, 01-mar-2020 DOI: https://doi.org/10.3390/computation8010015
D.Fogel, “Evolutionary computation - toward a new philosophy of machine intelligence”, 1995
J.C. Bezdek, “Computational Intelligence Defined - By Everyone !”, en Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Springer Berlin Heidelberg, 1998, pp. 10–37 DOI: https://doi.org/10.1007/978-3-642-58930-0_2
D. Poole, A. Mackworth, y R. Goebel, Computational Intelligence: A Logical Approach. New York: Oxford University Press, 1998
A. Kusiak, “Computational Intelligence in Design and Manufacturing”, Assem. Autom., vol. 21, núm. 3, pp. 275–276, sep. 2001 DOI: https://doi.org/10.1108/aa.2001.21.3.275.1
S. Dick y A. Kandel, Computational Intelligence in Software Quality Assurance, vol. 63. WORLD SCIENTIFIC, 2005 DOI: https://doi.org/10.1142/9789812703477
Y. Q. Chen y Z. Yi, “Computational intelligence in control engineering”, Int. J. Robust Nonlinear Control, vol. 14, núm. 12, pp. 1081–1083, ago. 2004 DOI: https://doi.org/10.1002/rnc.893
A.P. Engelbrecht, Computational Intelligence: An Introduction: Second Edition. John Wiley and Sons, 2007 DOI: https://doi.org/10.1002/9780470512517
D. P. Hudedagaddi y B. K. Tripathy, “Quantum inspired computational intelligent techniques in image segmentation”, en Quantum Inspired Computational Intelligence: Research and Applications, Elsevier Inc., 2017, pp. 233–258 DOI: https://doi.org/10.1016/B978-0-12-804409-4.00007-3
T. Hanne y R. Dornberger, “Adapting the Teaching of Computational Intelligence Techniques to Improve Learning Outcomes”, en Studies in Systems, Decision and Control, vol. 294, Springer, 2021, pp. 113–129 DOI: https://doi.org/10.1007/978-3-030-48332-6_8
N. Ansari y N. Hou, Computational Intelligence for Optimization , 1a ed. New York: Springe US, 1997 DOI: https://doi.org/10.1007/978-1-4615-6331-0_1
A. E. Hassanien, A. Abraham, y F. Herrera, Foundations of Computational Intelligence , vol. 2. 2009 DOI: https://doi.org/10.1007/978-3-642-01533-5
A. Kroll y R. Mikut, “Computational Intelligence”, At-Automatisierungstechnik, vol. 56, núm. 7, pp. 335–338, ene. 2008 DOI: https://doi.org/10.1524/auto.2008.9055
R. C. Eberhart y Y. Shi, Computational Intelligence. Elsevier Inc., 2007 DOI: https://doi.org/10.1016/B978-155860759-0/50002-0
This article presents those results derived from a systematic review of the literature focused on the recognition of computational intelligence worldwide. The methodological development was constituted in four different facets: - the elaboration of search equations, - the verification of the quality and relevance of the documents, - the elaboration of schemes for the recognition of the trends, - the investigation of confluences and differences. 55 articles from the Scopus® and Web of Science® databases were examined in VOSviewer® software for the construction and visualization of bibliometric networks and term concurrence; resulting in machine learning, classification, artificial intelligence, feature selection, algorithm classification, neural networks, intelligent systems among others. This study yields results from various fields of evolutionary learning for data collection to provide new and diverse faster applications, allowing data optimization and new prediction strategies or tools.
K. Börner, C. Chen, y K. W. Boyack, “Visualizing knowledge domains”, Annu. Rev. Inf. Sci. Technol., vol. 37, pp. 179–255, 2003 DOI: https://doi.org/10.1002/aris.1440370106
C. Okoli y K. Schabram, “Guide to Conducting a Systematic Literature Review of Information Systems Research”, Work. Pap. Inf. Syst., 2010 DOI: https://doi.org/10.2139/ssrn.1954824
M. Dixon-Woods, S. Agarwal, D. Jones, B. Young, y A. Sutton, “Synthesising qualitative and quantitative evidence: a review of possible methods”, J. Heal. Serv. Res. Policy, vol. 10, núm. 1, pp. 45–53, ene. 2005 DOI: https://doi.org/10.1177/135581960501000110
A. J. Onwuegbuzie, N. L. Leech, y K. M. T. Collins, “Qualitative Analysis Techniques for the Review of the Literature”, 2012
M. J. Grant y A. Booth, “A typology of reviews: an analysis of 14 review types and associated methodologies”, Heal. Inf. Libr. J., vol. 26, núm. 2, pp. 91–108, jun. 2009 DOI: https://doi.org/10.1111/j.1471-1842.2009.00848.x
A. Christoforou y A. S. Andreou, “A Multilayer Fuzzy Cognitive Maps approach to the cloud adoption decision support problem”, en IEEE International Conference on Fuzzy Systems, 2015, vol. 2015-November, pp. 1–8 DOI: https://doi.org/10.1109/FUZZ-IEEE.2015.7338056
S. S. Ge, T. M. Guerra, F. L. Lewis, J. C. Principe, y M. Colnarič, “Computational Intelligence in Control”, IFAC Proc. Vol., vol. 47, núm. 3, pp. 8867–8878, 2014 DOI: https://doi.org/10.3182/20140824-6-ZA-1003.01164
S. Saeedi, R. Chavarriaga, R. Leeb, y J. D. R. Millan, “Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability”, IEEE Comput. Intell. Mag., vol. 11, núm. 1, pp. 32–39, feb. 2016 DOI: https://doi.org/10.1109/MCI.2015.2501550
L. Wang, Q. Zhang, A. Zhou, M. Gong, y L. Jiao, “Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm”, IEEE Trans. Evol. Comput., vol. 20, núm. 3, pp. 475–480, jun. 2016 DOI: https://doi.org/10.1109/TEVC.2015.2457616
V. Piuri, F. Scotti, y M. Roveri, “Computational intelligence in industrial quality control”, en IEEE International Workshop on Intelligent Signal Processing - Proceedings, 2005, pp. 4–9 DOI: https://doi.org/10.1109/WISP.2005.1531623
E. Lakshika y M. Barlow, “Computational Intelligence Approaches to Computational Aesthetics”, 2018, pp. 81–92 DOI: https://doi.org/10.4018/978-1-5225-7368-5.ch007
M. Frank, D. Drikakis, y V. Charissis, “Machine-learning methods for computational science and engineering”, Computation, vol. 8, núm. 1. MDPI Multidisciplinary Digital Publishing Institute, 01-mar-2020 DOI: https://doi.org/10.3390/computation8010015
D.Fogel, “Evolutionary computation - toward a new philosophy of machine intelligence”, 1995
J.C. Bezdek, “Computational Intelligence Defined - By Everyone !”, en Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Springer Berlin Heidelberg, 1998, pp. 10–37 DOI: https://doi.org/10.1007/978-3-642-58930-0_2
D. Poole, A. Mackworth, y R. Goebel, Computational Intelligence: A Logical Approach. New York: Oxford University Press, 1998
A. Kusiak, “Computational Intelligence in Design and Manufacturing”, Assem. Autom., vol. 21, núm. 3, pp. 275–276, sep. 2001 DOI: https://doi.org/10.1108/aa.2001.21.3.275.1
S. Dick y A. Kandel, Computational Intelligence in Software Quality Assurance, vol. 63. WORLD SCIENTIFIC, 2005 DOI: https://doi.org/10.1142/9789812703477
Y. Q. Chen y Z. Yi, “Computational intelligence in control engineering”, Int. J. Robust Nonlinear Control, vol. 14, núm. 12, pp. 1081–1083, ago. 2004 DOI: https://doi.org/10.1002/rnc.893
A.P. Engelbrecht, Computational Intelligence: An Introduction: Second Edition. John Wiley and Sons, 2007 DOI: https://doi.org/10.1002/9780470512517
D. P. Hudedagaddi y B. K. Tripathy, “Quantum inspired computational intelligent techniques in image segmentation”, en Quantum Inspired Computational Intelligence: Research and Applications, Elsevier Inc., 2017, pp. 233–258 DOI: https://doi.org/10.1016/B978-0-12-804409-4.00007-3
T. Hanne y R. Dornberger, “Adapting the Teaching of Computational Intelligence Techniques to Improve Learning Outcomes”, en Studies in Systems, Decision and Control, vol. 294, Springer, 2021, pp. 113–129 DOI: https://doi.org/10.1007/978-3-030-48332-6_8
N. Ansari y N. Hou, Computational Intelligence for Optimization , 1a ed. New York: Springe US, 1997 DOI: https://doi.org/10.1007/978-1-4615-6331-0_1
A. E. Hassanien, A. Abraham, y F. Herrera, Foundations of Computational Intelligence , vol. 2. 2009 DOI: https://doi.org/10.1007/978-3-642-01533-5
A. Kroll y R. Mikut, “Computational Intelligence”, At-Automatisierungstechnik, vol. 56, núm. 7, pp. 335–338, ene. 2008 DOI: https://doi.org/10.1524/auto.2008.9055
R. C. Eberhart y Y. Shi, Computational Intelligence. Elsevier Inc., 2007 DOI: https://doi.org/10.1016/B978-155860759-0/50002-0
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