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.
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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
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DOI: https://doi.org/10.1007/978-3-030-31129-2_45
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