Human Activity Recognition Using Parallel Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Silva:2021:CEC,
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author = "Bruno M. P. Silva and Heder S. Bernardino and
Helio J. C. Barbosa",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Human Activity Recognition Using Parallel Cartesian
Genetic Programming",
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year = "2021",
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editor = "Yew-Soon Ong",
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pages = "474--481",
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address = "Krakow, Poland",
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month = "28 " # jun # "-1 " # jul,
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isbn13 = "978-1-7281-8393-0",
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abstract = "Human activity recognition (HAR) is applicable to a
wide range of real-life situations. While machine
learning algorithms can be applied for solving this
problem, difficulties remain, such as handling a large
amount of data available for training and selecting the
most appropriate features. Hence, the advent of methods
to reduce these issues and improve the currently
available algorithms is relevant. Thus, we propose here
the application of Cartesian Genetic Programming of
Artificial Neural Networks (CGPANN) for training models
for HAR. As the computational cost is a relevant issue
in this context, high-performance computing strategies
in graphic processing units (GPU) are proposed for
CGPANN. Two computational experiments are executed and
the results show a decrease in computational time spent
with the usage of different data structures for the
parallel CGPANN on the GPU. Moreover, the CGPANN models
for HAR are promising when compared to results from the
literature.",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Training, Machine learning
algorithms, Computational modeling, Graphics processing
units, Evolutionary computation, Artificial neural
networks, Activity Recognition, Machine Learning,
Parallel Computing, GPU",
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DOI = "doi:10.1109/CEC45853.2021.9504793",
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notes = "Also known as \cite{9504793}",
- }
Genetic Programming entries for
Bruno Marcos Pinheiro da Silva
Heder Soares Bernardino
Helio J C Barbosa
Citations