# Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition
## 2 Materials and Methods
### Proposed Model
#### 2.2.2 Classification and the Proposed AWV Algorithm
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Authors** & **Works** & **Accuracy** \\ \hline \multirow{8}{*}{ASL recognition} & [PERSON] et al., 2018 [26] & Transfer learning using WRN (Wide Residual Networks) on ASL alphabet & 93.30\% \\ \cline{2-4}  & [PERSON] and [PERSON], 2019 [27] & Compare custom CNN model and transfer learning using MobileNetV2 on ASLs & 97.06\% \\ \cline{2-4}  & [PERSON] et al., 2020 [13] & Deep learning classification on ASL MNIST dataset & 97.62\% \\ \cline{2-4}  & [PERSON] et al., 2021 [28] & 2D-CNN with joints encoding hand gesture recognition for 14-class ASL alphabet & 96.31\% \\ \cline{2-4}  & [PERSON] et al., 2022 [25] & Four different sequences of LSTM and GRU for & 95.3\% \\ \cline{2-4}  & [PERSON] et al., 2022 [29] & Two stream fusion CNN for ASL & 97.57\% \\ \cline{2-4}  & 2022 [29] & alphabet recognition & 98.83\% (ASL Alphabet) \\ \cline{2-4}  & Proposed Work & The ARS-MA model on 29 classes of ASL Alphabet & 98.83\% (ASL Alphabet) \\ \cline{2-4}  & (ARS-MA) & and ASLA datasets, respectively & 98.79\% (ASLA) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._

## References

* World Health Organization (WHO) (2022) World Health Organization (WHO). Available online: www.who.int/deafness/world-hearing-day/whd-2018/en (accessed on 1 February 2022). * [PERSON] and [PERSON] (2020) [PERSON]; [PERSON]; [PERSON] Static Hand Gesture Recognition for American Sign Language using Deep Convolutional Neural Network. _IEEE Sens._**2020**, _11_, 2. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Technical approaches to Chinese sign language processing: A review. _IEEE Access_**2019**, \(7\), 96926-96935. [CrossRef]
* National Institute on Deafness and Other Communication Disorders (NIDCD) (2022). Available online: [[https://www.nidcd.nih.gov/health/american-sign-language](https://www.nidcd.nih.gov/health/american-sign-language)]([https://www.nidcd.nih.gov/health/american-sign-language](https://www.nidcd.nih.gov/health/american-sign-language)) (accessed on 7 June 2022). * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Sign Language Recognition: A Deep Survey. _Expert Syst. Appl._**2020**, _164_, 113794. [CrossRef]
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* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatiotemporal Feature Descriptor for Micro-Expression Recognition Using Local Cube Binary Pattern. _IEEE Access_**2019**, \(7\), 159214-159225. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] CNN-Based Network Intrusion Detection against Denial-of-Service Attacks. _Electronics_**2020**, \(9\), 916. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] [PERSON] Real-time vermacular sign language recognition using mediapipe and machine learning. _ISSN_**2021**, _2582_, 7421. * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON] American sign language recognition using leap motion sensor. In Proceedings of the 2014 13 th International Conference on Machine Learning and Applications, Detroit, MI, USA, 3-5 December 2014; pp. 541-544. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] An efficient sign language recognition (SLR) system using Camshift tracker and hidden Markov model (hmm). _SN Comput. Sci._**2021**, \(2\), 1-15. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Vision based Hand Gesture Recognition using Dynamic Time Warping for Indian Sign Language. In Proceedings of the 2016 international conference on information science (ICIS), Dublin, Ireland, 11-14 December 2017; pp. 120-125. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]. Classification of sign language characters by applying a deep convolutional neural network.