References
- Zhou, H., Zhou, W., Zhou, Y., & Li, H. (2020, April). Spatial-temporal multi-cue network for continuous sign language recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 13009-13016).
- Mittal, A., Kumar, P., Roy, P. P., Balasubramanian, R., &Chaudhuri, B. B. (2019). A modified LSTM model for continuous sign language recognition using leap motion. IEEE Sensors Journal, 19(16), 7056-7063. https://doi.org/10.1109/JSEN.2019.2909837
- El Ghoul, O., & Othman, A. (2022, March). Virtual reality for educating Sign Language using signing avatar: The future of creative learning for deaf students. In 2022 IEEE Global Engineering Education Conference (EDUCON) (pp. 1269-1274). IEEE.
- Muzata, K. K. (2020). Interrogating parental participation in the education and general development of their deaf children in Zambia. Afr. Disability Rts. YB, 8, 71.
- Singh, A., Singh, S. K., & Mittal, A. (2022). A review on dataset acquisition techniques in gesture recognition from Indian sign language. Advances in Data Computing, Communication and Security: Proceedings of I3CS2021, 305-313.
- Rastgoo, R., Kiani, K., &Escalera, S. (2021). Sign language recognition: A deep survey. Expert Systems with Applications, 164, 113794.
- Camgoz, N. C., Koller, O., Hadfield, S., & Bowden, R. (2020). Sign language transformers: Joint end-to-end sign language recognition and translation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10023-10033).
- Mittal, A., Kumar, P., Roy, P. P., Balasubramanian, R., &Chaudhuri, B. B. (2019). A modified LSTM model for continuous sign language recognition using leap motion. IEEE Sensors Journal, 19(16), 7056-7063. https://doi.org/10.1109/JSEN.2019.2909837
- Li, D., Rodriguez, C., Yu, X., & Li, H. (2020). Word-level deep sign language recognition from video: A new largescale dataset and methods comparison. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 1459-1469).
- Wadhawan, A., & Kumar, P. (2020). Deep learning-based sign language recognition system for static signs. Neural computing and applications, 32, 7957-7968.
- Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., &Grundmann, M. (2020). Blazepose: On-device real-time body pose tracking. arXiv preprint arXiv:2006.10204.
- Krebs, J., Roehm, D., Wilbur, R. B., &Malaia, E. A. (2021). Age of sign language acquisition has lifelong effect on syntactic preferences in sign language users. International journal of behavioral development, 45(5), 397-408. https://doi.org/10.1177/0165025420958193
- De Coster, M., Shterionov, D., Van Herreweghe, M., &Dambre, J. (2023). Machine translation from signed to spoken languages: State of the art and challenges. Universal Access in the Information Society, 1-27.
- JyothiRatnam, D., Soman, K. P., Bijimol, T. K., Priya, M. G., &Premjith, B. (2021). Hybrid machine translation system for the translation of simple english prepositions and periphrastic causative constructions from english to hindi. Applications in Ubiquitous Computing, 247-263.
- Murtagh, I., Nogales, V. U., &Blat, J. (2022, September). Sign language machine translation and the sign language lexicon: A linguistically informed approach. In Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track) (pp. 240-251).
- Angelova, G., Avramidis, E., &Moller, S. (2022, May). Using neural machine translation methods for sign language translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (pp. 273-284).
- Nair, J., Nithya, R., &VinodJincy, M. K. (2020). Design of a morphological generator for an English to Indian languages in a declension rule-based machine translation system. In Advances in Electrical and Computer Technologies: Select Proceedings of ICAECT 2019 (pp. 247-258). Springer Singapore.
- Othman, A., &Jemni, M. (2019). Designing high accuracy statistical machine translation for sign language using parallel corpus: case study English and American Sign Language. Journal of Information Technology Research (JITR), 12(2), 134-158. https://doi.org/10.4018/JITR.2019040108
- Seligman, M. (2019). The evolving treatment of semantics in machine translation. Adv. Empir. Transl. Stud. Dev. Transl. Resour. Technol., 53.
- Chen, K., Wang, R., Utiyama, M., &Sumita, E. (2020, July). Content word aware neural machine translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 358-364).
- Le, D. N., Nguyen, G. N., Bhateja, V., & Satapathy, S. C. (2017). Optimizing feature selection in video-based recognition using Max-Min Ant System for the online video contextual advertisement user-oriented system. Journal of computational science, 21, 361-370.
- AL-kubaisy, W. J., Yousif, M., Al-Khateeb, B., Mahmood, M., & Le, D. N. (2021). The red colobuses monkey: a new nature-inspired metaheuristic optimization algorithm. Int J Comput Intell Syst, 14(1), 1108-1118. https://doi.org/10.2991/ijcis.d.210301.004
- Le, D. N. (2017). A New Ant Algorithm for Optimal Service Selection with End-to-End QoS Constraints. Journal of Internet Technology, 18(5), 1017-1030.
- Dey, A., Biswas, S., & Le, D. N. (2023). Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention. International Journal of Advanced Computer Science and Applications, 14(10).
- Dey, A., Biswas, S., & Le, D. N (2024). Workout action recognition in video streams using an attention driven residual DC-GRU network. Computers, Materials & Continua, 79(2), 3067-3087. https://doi.org/10.32604/cmc.2024.049512