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Computers and Computing

A Weighted Deep Ensemble for Indian Sign Language Recognition

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References

  • J. Murray. “World Federation of the Deaf,” Rome, Italy, 2018. Available: http://wfdeaf.org/our-work/
  • U. Farooq, M. S. M. Rahim, N. Sabir, A. Hussain, and A. Abid, “Advances in machine translation for sign language: approaches, limitations, and challenges,” Neural Comput. Appl., Vol. 33, pp. 1–43, 2021. DOI:10.1007/s00521-021-06079-3.
  • M. Zandigohar, et al. “Multimodal fusion of EMG and vision for human grasp intent inference in prosthetic hand control,” arXiv:2104.03893, 2021.
  • S. Afrin, H. Mahmud, and M. K. Hasan, “EMG-based hand gesture dataset to control electronic wheelchair for SCI patients,” in Proceedings of the 2nd International Conference on Computing Advancements, 2022, pp. 165–70.
  • Y. Jiang, M. Zhao, C. Wang, F. Wei, K. Wang, and H. Qi, “Diver’s hand gesture recognition and segmentation for human–robot interaction on AUV,” Signal. Image. Video. Process., Vol. 15, pp. 1899–1906, 2021. DOI:10.1007/s11760-021-01930-5.
  • V. Bijalwan, V. B. Semwal, G. Singh, and R. G. Crespo, “Heterogeneous computing model for post-injury walking pattern restoration and postural stability rehabilitation exercise recognition,” Expert Syst., Vol. 39, no. 6, p. e12706, 2021. DOI:10.1111/exsy.12706.
  • A. A. Barbhuiya, R. K. Karsh, and R. Jain, “CNN based feature extraction and classification for sign language,” Multimed. Tools. Appl., Vol. 80, pp. 3051–69, 2021. DOI:10.1007/s11042-020-09829-y.
  • R. Rastgoo, K. Kiani, and S. Escalera, “Video-based isolated hand sign language recognition using a deep cascaded model,” Multimed. Tools. Appl., Vol. 79, pp. 22965–87, 2020. DOI:10.1007/s11042-020-09048-5.
  • O. K. Oyedotun, and A. Khashman, “Deep learning in vision-based static hand gesture recognition,” Neural Comput. Appl., Vol. 28, pp. 3941–51, 2017. DOI:10.1007/s00521-016-2294-8.
  • M. A. Ahmed, et al., “Real-time sign language framework based on wearable device: Analysis of MSL, DataGlove, and gesture recognition,” Soft. Comput., Vol. 25, pp. 11101–22, 2021. DOI:10.1007/s00500-021-05855-6.
  • J. Gałka, M. Mąsior, M. Zaborski, and K. Barczewska, “Inertial motion sensing glove for sign language gesture acquisition and recognition,” IEEE Sensors J., Vol. 16, no. 16, pp. 6310–16, 2016. DOI:10.1109/JSEN.2016.2583542.
  • S. A. Khomami, and S. Shamekhi, “Persian sign language recognition using IMU and surface EMG sensors,” Measurement (Mahwah NJ), Vol. 168, p. 108471, 2021. DOI:10.1016/j.measurement.2020.108471.
  • R. Gupta, and S. Rajan, “Comparative analysis of convolution neural network models for continuous Indian sign language classification,” Procedia. Comput. Sci., Vol. 171, pp. 1542–50, 2020. DOI:10.1016/j.procs.2020.04.165.
  • R. Gupta, and A. Kumar, “Indian sign language recognition using wearable sensors and multi-label classification,” Comput. Electr. Eng., Vol. 90, p. 106898, 2021. DOI:10.1016/j.compeleceng.2020.106898.
  • S. Sharma, and R. Gupta, “On the use of temporal and spectral central moments of forearm surface EMG for finger gesture classification,” in IEEE 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), 2018, pp. 234–9. DOI:10.1109/ICMETE.2018.00059.
  • R. Gupta, “On the selection of number of sensors for a wearable sign language recognition system,” in IEEE Twelfth International Conference on Contemporary Computing (IC3), 2019, pp. 1–6. DOI:10.1109/IC3.2019.8844894.
  • S. Sharma, R. Gupta, and A. Kumar, “Trbaggboost: An ensemble-based transfer learning method applied to Indian sign language recognition,” J. Ambient Intell. Human Comput., Vol. 13, pp. 3527–3537, 2020. DOI:10.1007/s12652-020-01979-z.
  • Q. Zhang, D. Wang, R. Zhao, and Y. Yu, “Myosign: Enabling end-to-end sign language recognition with wearables,” in Proceedings of the 24th International Conference on Intelligent User Interfaces, 2019, pp. 650–60. DOI:10.1145/3301275.3302296.
  • Y. Yu, X. Chen, S. Cao, X. Zhang, and X. Chen, “Exploration of Chinese sign language recognition using wearable sensors based on deep belief net,” IEEE. J. Biomed. Health. Inform., Vol. 24, no. 5, pp. 1310–20, 2020. DOI:10.1109/JBHI.2019.2941535.
  • Q. Fu, J. Fu, S. Zhang, X. Li, J. Guo, and S. Guo, “Design of intelligent human-computer interaction system for hard of hearing and non-disabled people,” IEEE Sensors J., Vol. 21, no. 20, pp. 23471–9, 2021. DOI:10.1109/JSEN.2021.3107949.
  • R. Gupta, “Stacking ensemble of convolutional neural networks for sign language recognition,” in 2022 International Conference on Computer Communication and Informatics (ICCCI), 2022, pp. 1–5. DOI:10.1109/ICCCI54379.2022.9741017.
  • A. A. Barbhuiya, R. K. Karsh, and R. Jain, “ASL hand gesture classification and localization using deep ensemble neural network,” Arab. J. Sci. Eng., 2022. DOI:10.1007/s13369-022-07495-w.
  • A. Sharma, N. Sharma, Y. Saxena, A. Singh, and D. Sadhya, “Benchmarking deep neural network approaches for Indian sign language recognition,” Neural Comput. Appl., Vol. 33, no. 12, pp. 6685–96, 2021. DOI:10.1007/s00521-020-05448-8.
  • H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. A. Muller, “Deep learning for time series classification: A review,” Data. Min. Knowl. Discov., Vol. 33, no. 4, pp. 917–63, 2019. DOI:10.1007/s10618-019-00619-1.
  • R. Zhu, et al., “Efficient human activity recognition solving the confusing activities via deep ensemble learning,” IEEE. Access., Vol. 7, pp. 75490–9, 2019. DOI:10.1109/ACCESS.2019.2922104.
  • M. Dua, R. Singla, S. Raj, and A. Jangra, “Deep CNN models-based ensemble approach to driver drowsiness detection,” Neural Comput. Appl., Vol. 33, no. 8, pp. 3155–68, 2021. DOI:10.1007/s00521-020-05209-7.
  • E. Ayan, H. Erbay, and F. Varçın, “Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks,” Comput. Electron. Agric., Vol. 179, p. 105809, 2020. DOI:10.1016/j.compag.2020.105809.
  • Indian Sign Language Dictionary. Coimbatore: Ramakrishna Mission Vivekananda University, 2015. Available: http://indiansignlanguage.org
  • S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier,” Int. J. Cogn. Comput. Eng., Vol. 2, pp. 40–6, 2021. DOI:10.1016/j.ijcce.2021.01.001.
  • T. Eltaeib, and A. Mahmood, “Differential evolution: A survey and analysis,” Appl. Sci., Vol. 8, no. 10, pp. 1–25, 2018. DOI:10.3390/app8101945.

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