Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language
The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based AttentionModule (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study ha...
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my.unimas.ir.369322021-12-09T01:22:51Z http://ir.unimas.my/id/eprint/36932/ Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language Khan, Rehman Ullah Wong, Woei Sheng Ullah, Insaf Inam Ul Haq, Muhammad Mohamad Hardyman, Barawi Khan, Muhammad Asghar HT Communities. Classes. Races P Philology. Linguistics Q Science (General) The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based AttentionModule (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAMResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research. Tech Science Press 2021-12-07 Article PeerReviewed text en cc_by_nc http://ir.unimas.my/id/eprint/36932/3/TSP_CMC_45824.pdf Khan, Rehman Ullah and Wong, Woei Sheng and Ullah, Insaf and Inam Ul Haq, Muhammad and Mohamad Hardyman, Barawi and Khan, Muhammad Asghar (2021) Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language. CMC-Computers, Materials & Continua, 71 (2). pp. 2755-2772. ISSN 1546-2218 https://www.techscience.com/cmc/v71n2/45824 10.32604/cmc.2022.022471 |
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HT Communities. Classes. Races P Philology. Linguistics Q Science (General) Khan, Rehman Ullah Wong, Woei Sheng Ullah, Insaf Inam Ul Haq, Muhammad Mohamad Hardyman, Barawi Khan, Muhammad Asghar Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language |
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The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based AttentionModule (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition.
This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and
training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAMResNet
‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research. |
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Article |
author |
Khan, Rehman Ullah Wong, Woei Sheng Ullah, Insaf Inam Ul Haq, Muhammad Mohamad Hardyman, Barawi Khan, Muhammad Asghar |
author_facet |
Khan, Rehman Ullah Wong, Woei Sheng Ullah, Insaf Inam Ul Haq, Muhammad Mohamad Hardyman, Barawi Khan, Muhammad Asghar |
author_sort |
Khan, Rehman Ullah |
title |
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language |
title_short |
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language |
title_full |
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language |
title_fullStr |
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language |
title_full_unstemmed |
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language |
title_sort |
evaluating the efficiency of cbam-resnet using malaysian sign language |
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Tech Science Press |
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2021 |
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http://ir.unimas.my/id/eprint/36932/3/TSP_CMC_45824.pdf http://ir.unimas.my/id/eprint/36932/ https://www.techscience.com/cmc/v71n2/45824 |
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