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Received 19 November 2022, accepted 12 December 2022, date of publication 2 January 2023, date of current version 9 January 2023. Digital Object Identifier 10.1 109/ACCESS.2022.3233671 Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture E. RAJALAKSHMI1, R. ELAKKIYA 1,2, V. SUBRAMANIYASWAMY 1, L

. Although our approach yields competitive results, it is imperative to address the superior performance of the methods presented in these papers. The method proposed in the paper[30] leverages a hybrid neural network architecture to capture both spatial and temporal features, thereby achieving high accuracy rates for static and dynamic isolated Indian and Russian Sign Language recognition. Similarly, the approach introduced in the paper[29] focuses on multi-semantic discriminative feature learning using a hybrid deep neural architecture, leading to remarkable results in sign gesture recognition tasks

. Similarly, the approach introduced in the paper[29] focuses on multi-semantic discriminative feature learning using a hybrid deep neural architecture, leading to remarkable results in sign gesture recognition tasks. Upon careful analysis, we identify several key differences between our approach and those presented in papers[29], [30]. While our method also emphasizes capturing spatial-temporal contextual information and joint motion through a two-stream multistage graph convolution with attention and residual connection (GCAR) model, we hypothesize that the superior performance of the methods 34566 VOLUME 12, 2024

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition fh =f ( wf 1Xt +wf 2H(t−1) ) (10) rh =f ( wr1Xt +wr2H(t+1) ) (11) Bt =g (wO ∗fh +wO ∗rh) (12) The Attention layer is employed based on the signiﬁ- cance of the sign gesture’s sequential and temporal essence. Eq. 13 to Eq

. Appl., vol. 35, no. 17, pp. 12481–12493, Jun. 2023. [63] M. Hrz, I. Gruber, J. Kanis, M. Bohč ek, M. Hlavč, and Z. Krňoul, ‘‘One model is not enough: Ensembles for isolated sign language recognition,’’ Sensors, vol. 22, no. 13, p. 5043, Jul. 2022. [64] E. Rajalakshmi, R. Elakkiya, V . Subramaniyaswamy, L. P. Alexey, G. Mikhail, M. Bakaev, K. Kotecha, L. A. Gabralla, and A. Abraham, ‘‘Multi-semantic discriminative feature learning for sign gesture recog- nition using hybrid deep neural architecture,’’ IEEE Access, vol. 11, pp. 2226–2238, 2023. [65] M. Maruyama, S. Ghose, K. Inoue, P. Pratim Roy, M. Iwamura, and M

. Mikhail, M. Bakaev, K. Kotecha, L. A. Gabralla, and A. Abraham, ‘‘Multi-semantic discriminative feature learning for sign gesture recog- nition using hybrid deep neural architecture,’’ IEEE Access, vol. 11, pp. 2226–2238, 2023. [65] M. Maruyama, S. Ghose, K. Inoue, P. Pratim Roy, M. Iwamura, and M. Yoshioka, ‘‘Word-level sign language recognition with multi-stream neural networks focusing on local regions,’’ 2021, arXiv:2106.15989. [66] A. Tunga, S. V . Nuthalapati, and J. Wachs, ‘‘Pose-based sign language recognition using GCN and BERT,’’ in Proc. IEEE Winter Conf. Appl. Comput. Vis. Workshops (WACVW), Jan. 2021, pp. 31–40. [67] E. Rajalakshmi, R. Elakkiya, A. L. Prikhodko, M. G. Grif, M

. V. CONCLUSION AND FUTURE WORK A novel Sign Language Recognition system has been devel- oped by deploying a multi-semantic discriminative feature learning Deep Neural Net and spatial, temporal and sequen- tial feature learning. The proposed hDNN-SLR framework intends to extract semantic manual co-articulations and non- manual elements, which are the key components necessary for sign recognition. In addition, spatial, sequential, and temporal features are also considered for accurate recogni- tion. Furthermore, abstract and discriminative feature extrac- tion is also carried out to segregate genuine and non-useful gestures

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition KETAN KOTECHAis currently an Administrator and a Teacher of deep learning. He has exper- tise and experience in cutting-edge research and projects in AI and deep learning for the last 25 years. He has published over 100 widely in sev- eral excellent peer-reviewed journals on various topics ranging from cutting-edge AI, education policies, teaching-learning practices, and AI for all. His research interests include artiﬁcial intel- ligence, computer algorithms, machine learning, and deep learning

Question: who are the authors of the paper " Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture"

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