Abstract
Convolutional neural network is the deep learning model which has several hidden layers in contrast to feed-forward neural network. Modeling of CNN layers depends upon the dataset and very challenging task as several trials are required to select the CNN parameters. In our work, we presented an optimal solution to tune the hyperparameters of CNN architecture by clonal search algorithm (CSA). This is tested on a challenging dataset of EMNIST, which is enhanced from ML benchmark dataset of NIST. With the proposed algorithm, it is possible to get the accuracy up to 98.7%.
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Bohra, N., Bhatnagar, V. (2021). Tuning of CNN Architecture by CSA for EMNIST Data. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 135. Springer, Singapore. https://doi.org/10.1007/978-981-15-5421-6_6
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DOI: https://doi.org/10.1007/978-981-15-5421-6_6
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