Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
When answering questions about the main topics or themes, try to identify common subjects across the documents.
Provide specific information and avoid vague responses.

Context:
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

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

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

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

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition FIGURE 8. Performance of framework having trained with the multi-signer dataset with three different signer datasets and testing with the multi-signer dataset. FIGURE 9. Performance of framework having trained with the multi-signer dataset with five different signer datasets and validation with the multi-signer dataset. the third constraint. When comparing the three graphs with different multi-signer train-test ratios, we can see that the framework performs well and tends to attain convergence when the Network is well-trained with a multi-signer dataset. E

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition FIGURE 8. Performance of framework having trained with the multi-signer dataset with three different signer datasets and testing with the multi-signer dataset. FIGURE 9. Performance of framework having trained with the multi-signer dataset with five different signer datasets and validation with the multi-signer dataset. the third constraint. When comparing the three graphs with different multi-signer train-test ratios, we can see that the framework performs well and tends to attain convergence when the Network is well-trained with a multi-signer dataset. E

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

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

Question: tell me the names of all the conrtributors of this "Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture"

Provide a detailed answer based only on the given context: