Effects of dataset characteristics on the performance of fatigue detection for crane operators using hybrid deep neural networks Article Swipe

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Pengkun Liu , Hung-Lin Chi , Xiao Li , Jingjing Guo ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1016/j.autcon.2021.103901

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Convolutional Neural Network

Computer Science

Deep Learning

Artificial Intelligence

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Recurrent Neural Network

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Convolutional neural network Computer science Deep learning Artificial intelligence Frame (networking) Facial expression Artificial neural network Recurrent neural network Constraint (computer-aided design) Machine learning Engineering Mechanical engineering Telecommunications

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.autcon.2021.103901
PDF: https://www.sciencedirect.com/science/article/pii/S0926580521003526
OA Status: bronze
Cited By: 55
References: 76
Related Works: 10
OpenAlex ID: https://openalex.org/W3199443318

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DOI: https://doi.org/10.1016/j.autcon.2021.103901

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Title: Effects of dataset characteristics on the performance of fatigue detection for crane operators using hybrid deep neural networks

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-09-16

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Authors: Pengkun Liu, Hung-Lin Chi, Xiao Li, Jingjing Guo

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Landing page: https://doi.org/10.1016/j.autcon.2021.103901

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PDF URL: https://www.sciencedirect.com/science/article/pii/S0926580521003526

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Concepts: Convolutional neural network, Computer science, Deep learning, Artificial intelligence, Frame (networking), Facial expression, Artificial neural network, Recurrent neural network, Constraint (computer-aided design), Machine learning, Engineering, Mechanical engineering, Telecommunications

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Cited by: 55

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referenced_works_count	76
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