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Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments

At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making t...

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Autores principales: Torres-Hernández, Mayra A., Escobedo-Barajas, Miguel H., Guerrero-Osuna, Héctor A., Ibarra-Pérez, Teodoro, Solís-Sánchez, Luis O., Martínez-Blanco, Ma del R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422198/
https://www.ncbi.nlm.nih.gov/pubmed/37571718
http://dx.doi.org/10.3390/s23156935
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author Torres-Hernández, Mayra A.
Escobedo-Barajas, Miguel H.
Guerrero-Osuna, Héctor A.
Ibarra-Pérez, Teodoro
Solís-Sánchez, Luis O.
Martínez-Blanco, Ma del R.
author_facet Torres-Hernández, Mayra A.
Escobedo-Barajas, Miguel H.
Guerrero-Osuna, Héctor A.
Ibarra-Pérez, Teodoro
Solís-Sánchez, Luis O.
Martínez-Blanco, Ma del R.
author_sort Torres-Hernández, Mayra A.
collection PubMed
description At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.
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spelling pubmed-104221982023-08-13 Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments Torres-Hernández, Mayra A. Escobedo-Barajas, Miguel H. Guerrero-Osuna, Héctor A. Ibarra-Pérez, Teodoro Solís-Sánchez, Luis O. Martínez-Blanco, Ma del R. Sensors (Basel) Article At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems. MDPI 2023-08-04 /pmc/articles/PMC10422198/ /pubmed/37571718 http://dx.doi.org/10.3390/s23156935 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Torres-Hernández, Mayra A.
Escobedo-Barajas, Miguel H.
Guerrero-Osuna, Héctor A.
Ibarra-Pérez, Teodoro
Solís-Sánchez, Luis O.
Martínez-Blanco, Ma del R.
Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
title Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
title_full Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
title_fullStr Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
title_full_unstemmed Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
title_short Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
title_sort performance analysis of embedded multilayer perceptron artificial neural networks on smart cyber-physical systems for iot environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422198/
https://www.ncbi.nlm.nih.gov/pubmed/37571718
http://dx.doi.org/10.3390/s23156935
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