(Universitas Harapan Bangsa, Indonesia)(2) * Alfian Ma'arif
(Universitas Ahmad Dahlan, Indonesia)(3) Wahyu Rahmaniar
(National Taipei University of Technology, Taiwan)(4) Haris Imam Karim Fathurrahman
(Universitas Ahmad Dahlan, Indonesia)(5) Aufaclav Zatu Kusuma Frisky
(Technische Universität Wien, Austria)(6) Qazi Mazhar ul Haq
(National University of Sciences and Technology, Pakistan)*corresponding author
AbstractThe application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.
|
DOIhttps://doi.org/10.31763/ijrcs.v2i4.888 |
Article metrics10.31763/ijrcs.v2i4.888 Abstract views : 2917 | PDF views : 1192 | PDF views : 122 |
Cite |
Full Text Download
Full Text Download
|
References
[1] M. M. Khan, S. Hossain, P. Mozumdar, S. Akter, and R. H. Ashique, "A review on machine learning and deep learning for various antenna design applications," Heliyon, vol. 8, no. 4, p. e09317, 2022, https://doi.org/10.1016/j.heliyon.2022.e09317.
[2] M. Ancin, E. Pindado, and M. Sanchez, "New Trends in the Global Digital Transformation Process of the Agri-Food Sector: An Exploratory Study Based on Twitter," Agricultural Systems, vol. 203, p. 103520, 2022, https://doi.org/10.2139/ssrn.4093526.
[3] P. W. Tien, S. Wei, J. Darkwa, C. Wood, and J. K. Calautit, “Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review,” Energy AI, vol. 10, p. 100198, 2022, https://doi.org/10.1016/j.egyai.2022.100198.
[4] M. Parzinger, L. Hanfstaengl, F. Sigg, U. Spindler, U. Wellisch, and M. Wirnsberger, "Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection — Using machine learning methods Random Forest and LASSO," Build. Environ., vol. 223, p. 109313, 2022, https://doi.org/10.1016/j.buildenv.2022.109313.
[5] M. Zhu et al., "A review of the application of machine learning in water quality evaluation," Eco-Environment Heal., vol. 1, no. 2, pp. 107–116, 2022, https://doi.org/10.1016/j.eehl.2022.06.001.
[6] L. Zhang, L. Zhang, B. Du, J. You, and D. Tao, "Hyperspectral image unsupervised classification by robust manifold matrix factorization," Inf. Sci., vol. 485, pp. 154–169, 2019, https://doi.org/10.1016/j.ins.2019.02.008.
[7] G. Fu, Y. Jin, S. Sun, Z. Yuan, and D. Butler, "The role of deep learning in urban water management: A critical review," Water Res., vol. 223, p. 118973, 2022, https://doi.org/10.1016/j.watres.2022.118973.
[8] C. Shen, "A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists," Water Resour. Res., vol. 54, no. 11, pp. 8558–8593, 2018, https://doi.org/10.1029/2018WR022643.
[9] R. K. Mishra, G. Y. S. Reddy, and H. Pathak, "The Understanding of Deep Learning: A Comprehensive Review," Math. Probl. Eng., 2021, https://doi.org/10.1155/2021/5548884.
[10] M. Wu, X. Liu, N. Gui, X. Yang, J. Tu, S. Jiang, and Q. Zhao, "Prediction of remaining time and time interval of pebbles in pebble bed HTGRs aided by CNN via DEM datasets," Nucl. Eng. Technol., 2022, https://doi.org/10.1016/j.net.2022.09.019.
[11] M. M and S. P, "COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model," Adv. Eng. Softw., vol. 173, p. 103214, 2022, https://doi.org/10.1016/j.advengsoft.2022.103214.
[12] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, "A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects," IEEE Trans. Neural Networks Learn. Syst., pp. 1–21, 2021, https://doi.org/10.1109/TNNLS.2021.3084827.
[13] M. K. Bohmrah and H. Kaur, "Classification of Covid-19 patients using efficient fine-tuned deep learning DenseNet model," Glob. Transitions Proc., vol. 2, no. 2, pp. 476–483, 2021, https://doi.org/10.1016/j.gltp.2021.08.003.
[14] W. L. Mao, H. I. K. Fathurrahman, Y. Lee, and T. W. Chang, "EEG dataset classification using CNN method," Journal of physics: conference series, vol. 1456, no. 1, p. 012017, 2020, https://doi.org/10.1088/1742-6596/1456/1/012017.
[15] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artif. Intell. Rev., vol. 53, no. 8, pp. 5455–5516, 2020, https://doi.org/10.1007/s10462-020-09825-6.
[16] S. J. Shri and S. Jothilakshmi, "Crowd Video Event Classification using Convolutional Neural Network," Comput. Commun., vol. 147, pp. 35–39, 2019, https://doi.org/10.1016/j.comcom.2019.07.027.
[17] R. Roncancio, A. El Gamal, and J. P. Gore, "Turbulent flame image classification using Convolutional Neural Networks," Energy AI, vol. 10, p. 100193, 2022, https://doi.org/10.1016/j.egyai.2022.100193.
[18] T. Bezdan and N. Bačanin Džakula, "Convolutional Neural Network Layers and Architectures," International Scientific Conference on Information Technology and Data Related Research, pp. 445–451, 2019, https://doi.org/10.15308/Sinteza-2019-445-451.
[19] The Mathworks, Introducing Deep Learning with MATLAB, 2018, https://www.mathworks.com/campaigns/offers/deep-learning-with-matlab.html.
[20] S. A. Singh, T. G. Meitei, and S. Majumder, "Short PCG classification based on deep learning," Deep Learning Techniques for Biomedical and Health Informatics, Elsevier Inc., pp. 141–164, 2020, https://doi.org/10.1016/B978-0-12-819061-6.00006-9.
[21] S. A. Suha and T. F. Sanam, "A deep convolutional neural network-based approach for detecting burn severity from skin burn images," Mach. Learn. with Appl., vol. 9, no. April, p. 100371, 2022, https://doi.org/10.1016/j.mlwa.2022.100371.
[22] C. Ding, Y. Li, Y. Xia, L. Zhang, and Y. Zhang, "Automatic kernel size determination for deep neural networks based hyperspectral image classification," Remote Sens., vol. 10, no. 3, 2018, https://doi.org/10.3390/rs10030415.
[23] R. Riad, O. Teboul, D. Grangier, and N. Zeghidour, "Learning strides in convolutional neural networks," International Conference on Learning Representations, pp. 1–17, 2022, https://doi.org/10.31219/osf.io/4yz8f.
[24] A. Nguyen, S. Choi, W. Kim, S. Ahn, J. Kim, and S. Lee, "Distribution Padding in Convolutional Neural Networks," 2019 IEEE International Conference on Image Processing (ICIP), pp. 4275–4279, 2019, https://doi.org/10.1109/ICIP.2019.8803537.
[25] Q. Ke, J. Liu, M. Bennamoun, S. An, F. Sohel, and F. Boussaid, "Computer vision for human-machine interaction," Computer vision for human–machine interaction, Computer Vision For Assistive Healthcare, pp. 127–145, 2018, https://doi.org/10.1016/B978-0-12-813445-0.00005-8.
[26] D. Bhatt et al., "Cnn variants for computer vision: History, architecture, application, challenges and future scope," Electron., vol. 10, no. 20, p. 2470, 2021, https://doi.org/10.3390/s19010217.
[27] Z. J. Wang et al., "CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization," IEEE Trans. Vis. Comput. Graph., vol. 27, no. 2, pp. 1396–1406, 2021, https://doi.org/10.1162/neco_a_00990.
[28] L. Alzubaidi et al., "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions," Journal of Big Data, vol. 8, p. 83, 2021, https://doi.org/10.1186/s40537-021-00444-8.
[29] G. Wei, G. Li, J. Zhao, and A. He, "Development of a LeNet-5 gas identification CNN structure for electronic noses," Sensors, vol. 19, no. 1, pp. 1–17, 2019, https://doi.org/10.3390/s19010217.
[30] W. Rawat and Z. Wang, "Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review," Neural Comput., vol. 29, pp. 2352–2449, 2017, https://doi.org/10.1162/neco_a_00990.
[31] X. Han, Y. Zhong, L. Cao, and L. Zhang, "Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification," Remote Sens., vol. 9, no. 8, 2017, https://doi.org/10.3390/rs9080848.
[32] U. Muhammad, W. Wang, S. P. Chattha, and S. Ali, "Pre-trained VGGNet Architecture for Remote-Sensing Image Scene Classification," Proceedings - International Conference on Pattern Recognition, pp. 1622–1627, 2018, https://doi.org/10.1109/ICPR.2018.8545591.
[33] Q. Guan et al., "Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: A pilot study," J. Cancer, vol. 10, no. 20, pp. 4876–4882, 2019, https://doi.org/10.7150/jca.28769.
[34] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998, https://doi.org/10.1109/5.726791.
[35] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017, https://doi.org/10.1145/3065386.
[36] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–14, 2015, https://doi.org/10.48550/arXiv.1409.1556.
[37] M. Swapna, D. Y. K. Sharma, and D. B. Prasad, "CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net," Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 953–959, Mar. 2020, https://doi.org/10.35940/ijrte.F9532.038620.
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Alfian Ma'arif
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
| About the Journal | Journal Policies | Author | Information |
International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia