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Research Article

A Comparative Study on Performance of SVM and CNN in Tanzania Sign Language Translation Using Image Recognition

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Article: 2005297 | Received 29 Oct 2020, Accepted 08 Nov 2021, Published online: 14 Nov 2021

References

  • Caliwag, A., S. R. Angsanto, and W. Lim. 2018. Korean sign language translation using machine learning 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) (IEEE). 826–466 doi:10.1109/ICUFN.2018.8436747.
  • Chen, K.-C., C.-L. Chin, N.-C. Chung, and C.-L. Hsu. 2020. Combining multi-classifier with CNN in detection and classification of breast calcification. Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices Taipei, Taiwan 74 , 304–11. Springer, Chamdoi: 10.1007/978-3-030-30636-6_42.
  • Cheok, M. J., Z. Omar, and M. H. Jaward. 2017. A review of hand gesture and sign language recognition techniques. International Journal of Machine Learning and Cybernetics 10 (1):131–53. doi:10.1007/s13042-017-0705-5.
  • Fatmi, R., S. Rashad, and R. Integlia. 2019. Comparing ANN, SVM, and HMM based machine learning methods for American sign language recognition using wearable motion sensors. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019 7-9 Jan. 2019 (IEEE) Las Vegas, NV, USA, 290–97. doi:10.1109/CCWC.2019.8666491.
  • Gogul, I., and V. Sathiesh Kumar. 2017. Flower species recognition system using convolution neural networks and transfer learning. 2017 4th International Conference on Signal Processing, Communication and Networking, ICSCN 2017 16-18 March 2017 (IEEE) Chennai, India, 1–6. doi:10.1109/ICSCN.2017.8085675.
  • Hasan, H., Z. M. S. Helmi, and M. Habshi. 2019. A comparison between Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models for hyperspectral image classification. IOP Conference Series: Earth and Environmental Science 357 (November):012035. doi:10.1088/1755-1315/357/1/012035.
  • Hasan, M., S. Ullah, M. J. Khan, and K. Khurshid. 2019. Comparative analysis of SVM, ANN and CNN for classifying vegetation species using hyperspectral thermal infrared data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (2/W13):1861–68. doi:10.5194/isprs-archives-XLII-2-W13-1861-2019.
  • Jin, C. M., Z. Omar, and M. H. Jaward. 2016. A mobile application of American sign language translation via image processing algorithms. 2016 IEEE Region 10 Symposium (TENSYMP) 2016 (IEEE) Bali, Indonesia, 104–09. doi:10.1109/TENCONSpring.2016.7519386.
  • Kamal, S. M., Y. Chen, L. Shaozi, X. Shi, and J. Zheng. 2019. Technical approaches to Chinese sign language processing: A review. IEEE Access 7:96926–35. doi:10.1109/ACCESS.2019.2929174.
  • Kim, P. 2017. MATLAB deep learning. Berkeley, CA: Apress. doi:10.1007/978-1-4842-2845-6.
  • Kula, N. C., and L. Marten. 2008. Central, East, and Southern African languages. In One thousand languages, ed. P. Austin, 86–111. Oxford: The Ivy Press.
  • Mohammad, H., and S. M. Nasir. 2015. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process 5 (2):01–11. doi:10.5121/ijdkp.2015.5201.
  • Raschka, S. 2018. Model evaluation, model selection, and algorithm selection in machine learning. http://arxiv.org/abs/1811.12808.
  • Rashid Agha, R. A., N. S. Al Muhammed, and P. Fattah. 2018. A comprehensive study on sign languages recognition systems using (SVM, KNN, CNN and ANN). ACM International Conference Proceeding Series. doi:10.1145/3279996.3280024.
  • Sokolova, M., and G. Lapalme. 2009. A systematic analysis of performance measures for classification tasks. Information Processing & Management 45 (4):427–37. doi:10.1016/j.ipm.2009.03.002.
  • Tharwat, A., T. Gaber, H. M. Aboul Ella, K. Shahin, and B. Refaat. 2015. SIFT-based Arabic sign language recognition system. Advances in Intelligent Systems and Computing 334:359–70. doi:10.1007/978-3-319-13572-4_30.