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Implementation of Parallel Evolutionary Convolutional Neural Network for Classification in Human Activity and Image Recognition

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Advances in Computational Intelligence (MICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14391))

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Abstract

Pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components . Processing this volume of information allows a more exact classification in wider types of applications; however, some of the difficulties of this scheme is the maintenance of numerical precision and mainly the reduction of the execution time. During the last 15 years, several Machine Learning solutions have been implemented to reduce the number of pattern components to be analyzed, such as artificial neural networks. Deep learning is an appropriate tool to accomplish this task. In this paper, a convolutional neural network is implemented for recognition and classification of human activity signals and digital images. It is achieved by automatically adjusting the parameters of the neural network through genetic algorithms using a multiprocessor and GPU platform. The results obtained show the reduction of computational costs and the possibility of better understanding of the solutions provided by Deep Learning.

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Acknowledgements

We acknowledge the support of Project PID2020-115570GB-C21 funded by MCIN/AIE/10.13039/501100011033/ and Junta de Extremadura, Consejería de Economía e Infraestructuras, of the European Regional Development Fund, “Una manera de hacer Europa”, grant GR21108.

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Authors and Affiliations

  1. Departamento de Sistemas, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Azapotzalco. Av. Sn. Pablo 180, 02200, Col Reynosa Tamps., Mexico City, Mexico

    Juan Villegas-Cortez

  2. Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana, Unidad Iztapalapa, Iztapalapa, Mexico City, Mexico

    Graciela Román-Alonso & Yafte Aaron Flores-Morales

  3. Department of Computer Science, University of Extremadura, C/. Santa Teresa de Jornet, 38, 06800, Mérida, Spain

    Francisco Fernandez De Vega

  4. Department of Chemistry, Universidad Autónoma Metropolitana, Unidad Iztapalapa, Iztapalapa, Mexico City, Mexico

    Salomon Cordero-Sanchez

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  1. Juan Villegas-Cortez
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  2. Graciela Román-Alonso
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  3. Francisco Fernandez De Vega
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  4. Yafte Aaron Flores-Morales
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  5. Salomon Cordero-Sanchez
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Correspondence to Juan Villegas-Cortez .

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Editors and Affiliations

  1. Center for Computing Research, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, Mexico

    Hiram Calvo

  2. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Lourdes Martínez-Villaseñor

  3. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Hiram Ponce

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Villegas-Cortez, J., Román-Alonso, G., Fernandez De Vega, F., Flores-Morales, Y.A., Cordero-Sanchez, S. (2024). Implementation of Parallel Evolutionary Convolutional Neural Network for Classification in Human Activity and Image Recognition. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_24

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  • DOI: https://doi.org/10.1007/978-3-031-47765-2_24

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