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Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

Rehman Ullah Khan1,*, Woei Sheng Wong1, Insaf Ullah2, Fahad Algarni3, Muhammad Inam Ul Haq4, Mohamad Hardyman bin Barawi1, Muhammad Asghar Khan2

1 Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kuching, 94300, Malaysia
2 Hamdard Institute of Engineering and Technology, Islamabad, 44000, Pakistan
3 College of Computing and Information Technology, The University of Bisha, Bisha, 61922, Saudi Arabia
4 Department of Computer Sciences, Khushal Khan Khattak University, Karak, 27200, Pakistan

* Corresponding Author: Rehman Ullah Khan. Email: email

(This article belongs to this Special Issue: Soft Computing and Machine Learning for Predictive Data Analytics)

Computers, Materials & Continua 2022, 71(2), 2755-2772.


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 Attention Module (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. CBAM-ResNet ‘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.


Cite This Article

R. Ullah Khan, W. Sheng Wong, I. Ullah, F. Algarni, M. Inam Ul Haq et al., "Evaluating the efficiency of cbam-resnet using malaysian sign language," Computers, Materials & Continua, vol. 71, no.2, pp. 2755–2772, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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