Volume 4 • Issue 1 • PP: 07–11 • 2024
Incorporating Kernels into Convolutional Neural Networks for Enhanced Feature Extraction
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Abstract
The simulation was used to evaluate the method of kernel K , Neural network NN, Convolution Neural network CNN bys using (MINIST) data set. The accuracy of the method was tested and compared with the convolutional neural network as well as with the kernel function for the same input data (training and testing). The results of simulation showed that there is a high accuracy of the method, and at the same time there is a decreasing loss over the epochs, which indicates the. We note high smooth by method for recognize among features.
Keywords
References
[1] J. Wu, “Introduction to convolutional neural networks,” National Key Laboratory for Novel Software Technology, Nanjing University, China, vol. 5, p. 495, 2017.
[2] K. O’Shea and R. Nash, “An introduction to convolutional neural networks,” arXiv preprint arXiv:1511.08458, 2015.
[3] L. Anuj and M. T. Gopalakrishna, “ResNet50-YOLOv2- convolutional neural network based hybrid deep structural learning for moving vehicle tracking under occlusion,” Solid State Technology, vol. 63, pp. 3237–3258, 2020.
[4] Q. A. Al-Haija, M. Smadi, and O. M. Al-Bataineh, “Identifying phasic dopamine releases using DarkNet-19 convolutional neural network,” Proceedings of the 2021 IEEE International IoT, Electronics and Mechatronics Conference, pp. 1–5, 2021.
[5] D. Bhatt, C. Patel, H. Talsania, J. Patel, R. Vaghela, S. Pandya et al., “CNN variants for computer vision: History, architecture, application, challenges and future scope,” Electronics, vol. 10, p. 2470, 2021.
[6] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
[7] H. Zhang, “A review of convolutional neural network development in computer vision,” EAI Endorsed Transactions on Internet of Things, vol. 7, no. 28, p. e2, 2022.
[8] X. Ji, Q. Yan, D. Huang, B. Wu, X. Xu, A. Zhang et al., “Filtered selective search and evenly distributed convolutional neural networks for casting defects recognition,” Journal of Materials Processing Technology, vol. 292, p. 117064, 2021.
[9] C.-C. J. Kuo, “Understanding convolutional neural networks with a mathematical model,” Journal of Visual Communication and Image Representation, vol. 41, pp. 406–413, 2016.
[10] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: Analysis, applications, and prospects,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, pp. 6999–7019, 2021.
[11] T. N. Sainath, B. Kingsbury, A.-r. Mohamed, G. E. Dahl, G. Saon, H. Soltau et al., “Improvements to deep convolutional neural networks for LVCSR,” Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 315–320, 2013.
[12] S. Shetty, “Application of convolutional neural network for image classification on Pascal VOC Challenge 2012 dataset,” arXiv preprint arXiv:1607.03785, 2016.
[13] X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” Advances in Neural Information Processing Systems, vol. 28, pp. 802–810, 2015.
[14] T. Wiatowski and H. Bölcskei, “A mathematical theory of deep convolutional neural networks for feature extraction,” IEEE Transactions on Information Theory, vol. 64, pp. 1845–1866, 2018.
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