Computer big data analysis and predictive maintenance based on deep learning
Theoretical research results such as computer big data analysis and machine learning are essential support for the design of convenient and effective deep learning models, however, existing studies seldom viewed this problem from the perspectives of computer big data sampling, parallel processing op...
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International Information and Engineering Technology Association
2022
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Online Access: | http://psasir.upm.edu.my/id/eprint/100775/ https://www.iieta.org/journals/isi/paper/10.18280/isi.270220 |
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my.upm.eprints.1007752023-09-11T01:53:50Z http://psasir.upm.edu.my/id/eprint/100775/ Computer big data analysis and predictive maintenance based on deep learning Jing, Gao Ismail, Normala Yanjun, Gao Theoretical research results such as computer big data analysis and machine learning are essential support for the design of convenient and effective deep learning models, however, existing studies seldom viewed this problem from the perspectives of computer big data sampling, parallel processing optimization, data preprocessing, and predictive maintenance. To fill in this gap, this paper researched the computer big data analysis and predictive maintenance based on deep learning. At first, the paper elaborated on the self-adaptively adjusted sampling and parallel processing optimization of computer big data, and gave the flow of computer big data preprocessing based on a deep learning model; then, it introduced the computer big data analysis and predictive maintenance method based on deep learning; at last, experiments were conducted to compare the performance of different Convolutional Neural Network (CNN) models and the results proved the effectiveness of the proposed model. International Information and Engineering Technology Association 2022-04 Article PeerReviewed Jing, Gao and Ismail, Normala and Yanjun, Gao (2022) Computer big data analysis and predictive maintenance based on deep learning. Ingenierie des Systemes d'Information, 27 (2). 349 - 355. ISSN 16331311 https://www.iieta.org/journals/isi/paper/10.18280/isi.270220 10.18280/isi.270220 |
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Theoretical research results such as computer big data analysis and machine learning are essential support for the design of convenient and effective deep learning models, however, existing studies seldom viewed this problem from the perspectives of computer big data sampling, parallel processing optimization, data preprocessing, and predictive maintenance. To fill in this gap, this paper researched the computer big data analysis and predictive maintenance based on deep learning. At first, the paper elaborated on the self-adaptively adjusted sampling and parallel processing optimization of computer big data, and gave the flow of computer big data preprocessing based on a deep learning model; then, it introduced the computer big data analysis and predictive maintenance method based on deep learning; at last, experiments were conducted to compare the performance of different Convolutional Neural Network (CNN) models and the results proved the effectiveness of the proposed model. |
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Article |
author |
Jing, Gao Ismail, Normala Yanjun, Gao |
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Jing, Gao Ismail, Normala Yanjun, Gao Computer big data analysis and predictive maintenance based on deep learning |
author_facet |
Jing, Gao Ismail, Normala Yanjun, Gao |
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Jing, Gao |
title |
Computer big data analysis and predictive maintenance based on deep learning |
title_short |
Computer big data analysis and predictive maintenance based on deep learning |
title_full |
Computer big data analysis and predictive maintenance based on deep learning |
title_fullStr |
Computer big data analysis and predictive maintenance based on deep learning |
title_full_unstemmed |
Computer big data analysis and predictive maintenance based on deep learning |
title_sort |
computer big data analysis and predictive maintenance based on deep learning |
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International Information and Engineering Technology Association |
publishDate |
2022 |
url |
http://psasir.upm.edu.my/id/eprint/100775/ https://www.iieta.org/journals/isi/paper/10.18280/isi.270220 |
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13.160551 |