Study and Develop a Convolutional Neural Network for MNIST Handwritten Digit Classification-Reference-Cited by-同舟云学术

Study and Develop a Convolutional Neural Network for MNIST Handwritten Digit Classification

Published:2022-07-03 Issue: Volume: Page:407-416
ISSN:2367-3370
Container-title:Proceedings of Third International Conference on Computing, Communications, and Cyber-Security
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Author:

Jayswal Disha,Panchal Brijeshkumar Y.,Patel Bansari,Acharya Nidhi,Nayak Rikin,Goel Parth

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-1142-2_32

Reference18 articles.

1. Rudraswamimath, V.R., & Bhavanishankar, K. (2019). Handwritten digit recognition using CNN. International Journal of Innovative Science and Research Technology, 4, 182–187.

2. El Kessab, B., Daoui, C., Bouikhalene, B., Fakir, M., & Moro, K. (2013). Extraction method of handwritten digit recognition tested on the MNIST database. International Journal of Advanced Science and Technology, 50, 99–110.

3. LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P. & Vapnik, V. (1995). Comparison of learning algorithms for handwritten digit recognition. In International Conference on Artificial Neural Networks.

4. Abu Ghosh, M. M., & Maghari, A. Y. (2017). A comparative study on handwriting digit recognition using neural networks. In International Conference on Promising Electronic Technologies (ICPET) (pp. 77–81).

5. Bohara, M., Patel, K., Patel, B., & Desai, J. (2021, September). An AI based web portal for cotton price analysis and prediction. In 3rd International Conference on Integrated Intelligent Computing Communication and Security (ICIIC 2021) (pp. 33–39). Atlantis Press.

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