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All issues Volume 97 (2024) BIO Web Conf., 97 (2024) 00051 References
Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00051
Number of page(s) 9
DOI https://doi.org/10.1051/bioconf/20249700051
Published online 05 April 2024
  • M. S. Amin, S. T. H. Rizvi, and M. M. Hossain, “A Comparative Review on Applications of Different Sensors for Sign Language Recognition,” J. Imaging, 8, no. 4, 2022, DOI: 10.3390/jimaging8040098. [Google Scholar]
  • M. S. Amin, M. T. Amin, M. Y. Latif, A. A. Jathol, N. Ahmed, and M. I. N. Tarar, “Alphabetical Gesture Recognition of American Sign Language using E-Voice Smart Glove,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1–6. DOI: 10.1109/INMIC50486.2020.9318185. [Google Scholar]
  • S. A. Abdulkarim and A. P. Engelbrecht, “Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments,” Neural Comput. Appl., vol. 33, no. 7, pp. 2667–2683, 2021, DOI: 10.1007/s00521-020-05163-4. [CrossRef] [Google Scholar]
  • C. Jin, S. Jang, X. Sun, J. Li, and R. Christenson, “Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network,” J. Civ. Struct. Heal. Monit., vol. 6, no. 3, pp. 545–560, 2016, DOI: 10.1007/s13349-016-0173-8. [CrossRef] [Google Scholar]
  • M. S. Amin, S. T. H. Rizvi, A. Mazzei, and L. Anselma, “Assistive Data Glove for Isolated Static Postures Recognition in American Sign Language Using Neural Network,” Electron., 12, no. 8, 2023, DOI: 10.3390/electronics12081904. [Google Scholar]
  • A. K. Jayswal, “Sign Language Recognition System Using Deep-Learning for Deaf and Dumb,” Int. Res. J. Mod. Eng. Technol. Sci., no. 0, pp. 2294–2300, 2023, DOI: 10.56726/irjmets36063. [Google Scholar]
  • A. Tyagi and S. Bansal, “Hand Anatomy and Neural Network-Based Recognition for Sign Language,” IETE J. Res., 2023, DOI: 10.1080/03772063.2023.2171911. [Google Scholar]
  • A. H. Alrubayi et al., “A pattern recognition model for static gestures in malaysian sign language based on machine learning techniques,” Comput. Electr. Eng., Vol. 95, no. August, p. 107383, 2021, DOI: 10.1016/j.compeleceng.2021.107383. [CrossRef] [Google Scholar]
  • P. N. Huu, Q. T. Minh, and H. L. The, “An ANN-based gesture recognition algorithm for smart-home applications,” KSII Trans. Internet Inf. Syst., vol. 14, no. 5, pp. 1967–1983, 2020, DOI: 10.3837/tiis.2020.05.006. [Google Scholar]
  • A. K. Sahoo, P. K. Sarangi, and P. Goyal, “Indian Sign Language Recognition Using Soft Computing Techniques,” Mach. Vis. Insp. Syst.,Vol. 1, pp. 37–65, 2020, DOI: 10.1002/9781119682042.ch2. [Google Scholar]
  • E. B. Candrasari, L. Novamizanti, and S. Aulia, “Discrete Wavelet Transform on static hand gesture recognition,” J. Phys. Conf. Ser., 1367, no. 1, 2019, DOI: 10.1088/1742-6596/1367/1/012022. [CrossRef] [Google Scholar]
  • S. Ravi, S. Maloji, V. V. K. Polurie, and K. K. Eepuri, “Sign language recognition with multi feature fusion and ANN classifier,” Turkish J. Electr. Eng. Comput. Sci., vol. 26, no. 6, pp. 2871–2885, 2018, DOI: 10.3906/elk-1711-139. [Google Scholar]
  • R. D. Raj and A. Jasuja, “British Sign Language Recognition using HOG,” 2018 IEEE Int. Students’ Conf. Electr. Electron. Comput. Sci. SCEECS 2018, pp. 1–4, 2018, DOI: 10.1109/SCEECS.2018.8546967. [Google Scholar]
  • M. Xing, J. Hu, Z. Feng, Y. Su, W. Peng, and J. Zheng, “Dynamic hand gesture recognition using motion pattern and shape descriptors,” Multimed. Tools Appl., vol. 78, no. 8, pp. 10649–10672, 2019, DOI: 10.1007/s11042-018-6553-9. [CrossRef] [Google Scholar]
  • A. Thongtawee, O. Pinsanoh, and Y. Kitjaidure, “A Novel Feature Extraction for American Sign Language Recognition Using Webcam,” BMEiCON 2018 - 11th Biomed. Eng. Int. Conf., pp. 1–5, 2019, DOI: 10.1109/BMEiCON.2018.8609933. [Google Scholar]
  • A. A. Theodosiou and R. C. Read, “Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician,” J. Infect., vol. 87, no. 4, pp. 287–294, 2023, DOI: 10.1016/j.jinf.2023.07.006. [CrossRef] [Google Scholar]
  • H. J. Kim and S. W. Baek, “Application of Wearable Gloves for Assisted Learning of Sign Language Using Artificial Neural Networks,” Processes, 11, no. 4, 2023, DOI: 10.3390/pr11041065. [Google Scholar]
  • M. M. Balaha et al., “A vision-based deep learning approach for independent-users Arabic sign language interpretation,” Multimed. Tools Appl., vol. 82, no. 5, pp. 6807–6826, 2023, DOI: 10.1007/s11042-022-13423-9. [CrossRef] [Google Scholar]
  • H. Hameed et al., “Recognizing British Sign Language Using Deep Learning: A Contactless and PrivacyPreserving Approach,” IEEE Trans. Comput. Soc. Syst., vol. 10, no. 4, pp. 2090–2098, 2023, DOI: 10.1109/TCSS.2022.3210288. [CrossRef] [Google Scholar]
  • J. Shin et al., “Korean Sign Language Recognition Using Transformer-Based Deep Neural Network,” Appl. Sci., 13, no. 5, 2023, DOI: 10.3390/app13053029. [Google Scholar]
  • M. Mohammed, S. M. Kadhem, and C. Author, “Al-Salam Journal for Engineering and Technology Iraqi Sign Language Translator system using Deep Learning,” pp. 109–116, 2023. [Google Scholar]
  • D. Sethia, P. Singh, and B. Mohapatra, “Gesture Recognition for American Sign Language Using Pytorch and Convolutional Neural Network,” Lect. Notes Electr. Eng., Vol. 959, pp. 307–317, 2023, DOI: 10.1007/978-981-19-6581-4_24. [CrossRef] [Google Scholar]
  • E. Aldhahri et al., “Arabic Sign Language Recognition Using Convolutional Neural Network and MobileNet,” Arab. J. Sci. Eng., vol. 48, no. 2, pp. 2147–2154, 2023, DOI: 10.1007/s13369-022-07144-2. [CrossRef] [Google Scholar]
  • A. Venugopalan and R. Reghunadhan, “Applying Hybrid Deep Neural Network for the Recognition of Sign Language Words Used by the Deaf COVID-19 Patients,” Arab. J. Sci. Eng., vol. 48, no. 2, pp. 1349–1362, 2023, DOI: 10.1007/s13369-022-06843-0. [CrossRef] [PubMed] [Google Scholar]
  • W. Suliman, M. Deriche, H. Luqman, and M. Mohandes, “Arabic Sign Language Recognition Using Deep Machine Learning,” 2021 4th Int. Symp. Adv. Electr. Commun. Technol. ISAECT 2021, pp. 1–4, 2021, DOI: 10.1109/ISAECT53699.2021.9668405. [Google Scholar]
  • Y. S. Tan, K. M. Lim, and C. P. Lee, “Hand gesture recognition via enhanced densely connected convolutional neural network,” Expert Syst. Appl., Vol. 175, no. February, p. 114797, 2021, DOI: 10.1016/j.eswa.2021.114797. [CrossRef] [Google Scholar]
  • M. M. Kamruzzaman, “Arabic Sign Language Recognition and Generating Arabic Speech Using Convolutional Neural Network,” Wirel. Commun. Mob. Comput., Vol. 2020, 2020, DOI: 10.1155/2020/3685614. [CrossRef] [Google Scholar]
  • J. Yuan et al., “Lightning Whistler Wave Speech Recognition Based on Grey Wolf Optimization Algorithm,” Atmosphere (Basel)., 13, no. 11, 2022, DOI: 10.3390/atmos13111828. [Google Scholar]
  • N. Paharia, R. S. Jadon, and S. K. Gupta, “Optimization of convolutional neural network hyperparameters using improved competitive gray wolf optimizer for recognition of static signs of Indian Sign Language,” J. Electron. Imaging, vol. 32, no. 02, pp. 1–20, 2023, DOI: 10.1117/1.jei.32.2.023042. [CrossRef] [Google Scholar]
  • A. Sharma, S. Kaur, S. Vyas, and A. Nayyar, Optical Character Recognition Using Hybrid CRNN Based Lexicon-Free Approach with Grey Wolf Hyperparameter Optimization, no. Ccie. Springer Nature Singapore, 2023. DOI: 10.1007/978-981-99-2730-2_47. [Google Scholar]
  • J. R. Challapalli and N. Devarakonda, “A novel approach for optimization of convolution neural network with hybrid particle swarm and grey wolf algorithm for classification of Indian classical dances,” Knowl. Inf. Syst., vol. 64, no. 9, pp. 2411–2434, 2022, DOI: 10.1007/s10115-022-01707-3. [CrossRef] [PubMed] [Google Scholar]
  • Y. Zhang, Z. Jin, and Y. Chen, “Hybridizing grey wolf optimization with neural network algorithm for global numerical optimization problems,” Neural Comput. Appl., vol. 32, no. 14, pp. 10451–10470, 2020, DOI: 10.1007/s00521-019-04580-4. [CrossRef] [Google Scholar]
  • H. M. Ahmed, B. A. B. Youssef, A. S. Elkorany, A. A. Saleeb, and F. Abd El-Samie, “Hybrid gray wolf optimizer-artificial neural network classification approach for magnetic resonance brain images,” Appl. Opt., 57, no. 7, p. B25, 2018, DOI: 10.1364/ao.57.000b25. [CrossRef] [PubMed] [Google Scholar]

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