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Abodi, J. A., & Li, X. (2014). An effective approach to offline Arabic handwriting recognition. Computers Electrical Engineering, 40(6), 1883-1901. https://doi.org/10.1016/j.compeleceng.2014.04.014
Alharbi, A. (2018). A genetic-LVQ neural networks approach for handwritten Arabic character recognition. Artificial Intelligence Research, 7(2), 45-54. https://doi.org/10.5430/air.v7n2p43
Belabiod, A., & Belaïd, A. (2018). Line and word segmentation of Arabic handwritten documents using neural networks (Research Report LORIA). Université de Lorraine. https://hal.inria.fr/hal-01910559
Burrow, P. (2004), Arabic handwriting recognition (Master of Science). University of Edinburgh, UK. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.404&rep=rep1&type=pdf
Eladel, A., Ejbali, R., Zaied, M., & Amar, C. B. (2015). Dyadic multi-resolution analysis-based deep learning for Arabic handwritten character classification. In Proceedings of 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 807-812). IEEE Publishing.
Elleuch, M., Maalej, R., & Kherallah, M. (2016). A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. Procedia Computer Science, 80, 1712-1723. https://doi.org/10.1016/j.procs.2016.05.512
Goularas, D., & Kamis, S. (2019). Evaluation of deep learning techniques in sentiment analysis from Twitter data. In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) (pp. 12-17). IEEE Publishing. https://doi.org/10.1109/Deep-ML.2019.00011
Guellil, I., Saâdane, H., Azouaou, F., Gueni, B., & Nouvel, D. (2021). Arabic natural language processing: An overview. Journal of King Saud University, Computer and Information Sciences, 33(5), 497-507. https://doi.org/10.1016/j.jksuci.2019.02.006
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proocedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778). IEEE Publishing. https://doi.org/10.1109/CVPR.2016.90
Hussien, R. S., Elkhidir, A. A., & Elnourani, M. G. (2016). Optical character recognition of Arabic handwritten characters using neural network. In Proceedings of 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE) (pp. 456-461). IEEE Publishing. https://doi.org/10.1109/ICCNEEE.2015.7381412
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communication ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
Najadat, H. M., Alshboul, A. A., & Alabed, A. F. (2019). Arabic handwritten characters recognition using convolutional neural network. In Proccedings of 10th International Conference on Information and Communication Systems (ICICS) (pp. 147-151). IEEE Publishing. https://doi.org/10.1109/IACS.2019.8809122
Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M., Chen, S., & Iyengar, S. (2019). A survey on deep learning: Algorithms, techniques, and applications. ACM Computer Surveys, 51(5), 1-36. https://doi.org/10.1145/3234150
Razak, H. A., AlMisreb, A. A., Saleh, M. A., & Tahir, N. M. (2020a). Housebreaking crime gait pattern classification using artificial neural network and support vector machine. Journal of Theoretical and Applied Information Technology, 98(12), 2185-2198.
Razak, H. A., Saleh, M. A., & Tahir, N. M. (2020b). Review on anomalous gait behavior detection using machine learning algorithms. Bulletin of Electrical Engineering and Informatics, 9(5), 2090-2096. https://doi.org/10.11591/eei.v9i5.2255
Savchenko, A. V. (2020). Probabilistic neural network with complex exponential activation functions in image recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(2), 651-660. https://doi.org/10.1109/TNNLS.2019.2908973
Simonyan, K., & Zisserman, A. (2015, May 7-9). Very deep convolutional networks for large-scale image recognition. In Proceedings of 3rd International Conference on Learning Representations, (ICLR) 2015 (pp. 1-14). San Diego, USA. https://arxiv.org/abs/1409.1556
Szegedy, C., Liu, W., Jia, Y., Arbor, A., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1-9). IEEE Publishing. https://doi.org/10.1109/CVPR.2015.7298594
Wahdan, A., Sendeyah, A. L., Hantoobi, S. A., & Salloum, K. S. (2020). A systematic review of text classification research based on deep learning models in Arabic language. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6629-6643. http://dx.doi.org/10.11591/ijece.v10i6.pp6629-6643
Wang, G. (2019). The status of Chinese handwriting identification and the improvement of methodologies. Forensic Science Criminology, 4(1), 1-7. http://dx.doi.org/10.15761/FSC.1000129
Wang, P. (2020). Research and design of smart home speech recognition system based on deep learning. In 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), Chongqing, China (pp. 218-221). IEEE Publishing. https://doi.org/10.1109/CVIDL51233.2020.00-98
Wong, L. C., & Loh, W. P. (2019). Segregating offline and online handwriting for conditional classification analysis. IOP Conference Series: Materials Science and Engineering, 530, Article 012058. http://dx.doi.org/10.1088/1757-899X/530/1/012058
Yang, L., Hanneke, S., & Carbonell, J. (2013). A theory of transfer learning with applications to active learning. Machine Learning, 90(2), 161-189. http://dx.doi.org/10.1007/s10994-012-5310-y
Yildiz, A., Almisreb, A. A., Dzakmic, S., Tahir, N. M., Turaev, S., & Saleh, M. A. (2020). Banknotes counterfeit detection using deep transfer learning approach. International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 8115-8122. http://dx.doi.org/10.30534/ijatcse/2020/172952020
Younis, K. S. (2017). Arabic hand-written character recognition based on deep convolutional neural networks. Jordanian Journal of Computers and Information Technology (JJCIT), 3(3), 186-200.
ISSN 0128-7702
e-ISSN 2231-8534
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