Abstract
In this paper, transfer learning from a pretrained Convolutional Neural Network (CNN) model called VGG16 in conjunction with a new evolutionary optimization algorithm called social ski driver algorithm (SSD) were applied for optimizing some hyperparameters of the CNN model to improve the classification performance of the images which was produced by the SEM technique. The results of the proposed approach (VGG16-SSD) are compared with the manual search method. The obtained results showed that the proposed approach was able to find the best values for the CNN hyperparameters that helped to successfully classify around 89.37% of a test dataset consisting of SEM images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vijayarani, S., Sharmila, S.: Research in big data – an overview. Inform. Eng. Int. J. (IEIJ), 4(3) (2016)
Modarres, M.H., Aversa, R., Cozzini, S., Ciancio, R., Leto, A., Brandino, G.P.: Neural network for nanoscience scanning electron microscope image recognition. Sci. Rep. (2017)
Choudhary, O.P., Choudhary, O.P.: Scanning electron microscope: advantages and disadvantages in imaging components. Int. J. Curr. Microbiol. Appl. Sci. (IJCMAS) 6, 1877–1882 (2017)
Kaplonek, W., Nadolny, K.: Advanced desktop SEM used for measurement and analysis of the abrasive tool’s active surface. Acta Microscopica 22(3) (2013)
Zhou, L., Li, Q., Huo, G., Zhou, Y.: Image classification using biomimetic pattern recognition with convolutional neural networks features. Comput. Intell. Neurosci. 2017 (2017)
Albeahdili, H.M., Alwzwazy, H.A., Islam, N.E.: Robust convolutional neural networks for image recognition. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 6(11) (2015)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Albelwi, S., Mahmood, A.: A Framework for designing the architectures of deep convolutional neural networks. Entropy (2017)
Tharwat, A., Gabel, T.: Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Comput. Appl. (2019)
Kaur, P., Gosain, A.: Comparing the behavior of oversampling and undersampling approach of class imbalance learning by combining class imbalance problem with noise. In: Advances in Intelligent Systems and Computing. Springer, Singapore (2018)
Yıldırım, P.: Pattern classification with imbalanced and multiclass data for the prediction of albendazole adverse event outcomes. Procedia Comput. Sci. 83, 1013–1018 (2016)
Pedamonti, D.: Comparison of non-linear activation functions for deep neural networks on MNIST classification task, arXiv preprint arXiv:1804.02763v1 (2018)
Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv preprint arXiv:1502.03167v3 (2015)
Mohamad, R., Harun, H.: Enhancement of cross-entropy based stopping criteria via turning point indicator. In: 2017 7th International Conference on Modeling, Simulation, and Applied Optimization, I (2017)
Indolia, S., Kumar, A., Mishra, S.P., Asopa, P.: Conceptual understanding of convolutional neural network - a deep learning approach. Procedia Comput. Sci. 132, 679–688 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014)
Aversa, R., Modarres, M.H., Cozzini, S., Ciancio, R.: NFFA-EUROPE Project (2018). http://doi.org/10.23728/b2share.19cc2afd23e34b92b36a1dfd0113a89f
Liu, B., Zhang, Y., He, D., Li, Y.: Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(11) 2018
Maria Navin, J.R., Balaji, K.: Performance analysis of neural networks and support vector machines using confusion matrix. Int. J. Adv. Res. Sci. Eng. Technology. 3(5), 2106–2109 (2016)
Google Colab. https://colab.research.google.com. Accessed 16 June 2019
Keras, F.C.: Deep learning library for Theano and TensorFlow (2015). https://keras.io. Accessed 16 June 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ezzat, D., Taha, M.H.N., Hassanien, A.E. (2020). An Optimized Deep Convolutional Neural Network to Identify Nanoscience Scanning Electron Microscope Images Using Social Ski Driver Algorithm. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-31129-2_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31128-5
Online ISBN: 978-3-030-31129-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)