Impact of Tuning Parameters in Deep Convolutional Neural Network Using a Crack Image Dataset
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912412643360768 |
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| author | Zabin, Mahe Choi, Ho-Jin Islam, Md. Monirul Uddin, Jia |
| author_facet | Zabin, Mahe Choi, Ho-Jin Islam, Md. Monirul Uddin, Jia |
| contents | The performance of a classifier depends on the tuning of its parame ters. In this paper, we have experimented the impact of various tuning parameters on the performance of a deep convolutional neural network (DCNN). In the ex perimental evaluation, we have considered a DCNN classifier that consists of 2 convolutional layers (CL), 2 pooling layers (PL), 1 dropout, and a dense layer. To observe the impact of pooling, activation function, and optimizer tuning pa rameters, we utilized a crack image dataset having two classes: negative and pos itive. The experimental results demonstrate that with the maxpooling, the DCNN demonstrates its better performance for adam optimizer and tanh activation func tion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03184 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of Tuning Parameters in Deep Convolutional Neural Network Using a Crack Image Dataset Zabin, Mahe Choi, Ho-Jin Islam, Md. Monirul Uddin, Jia Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning The performance of a classifier depends on the tuning of its parame ters. In this paper, we have experimented the impact of various tuning parameters on the performance of a deep convolutional neural network (DCNN). In the ex perimental evaluation, we have considered a DCNN classifier that consists of 2 convolutional layers (CL), 2 pooling layers (PL), 1 dropout, and a dense layer. To observe the impact of pooling, activation function, and optimizer tuning pa rameters, we utilized a crack image dataset having two classes: negative and pos itive. The experimental results demonstrate that with the maxpooling, the DCNN demonstrates its better performance for adam optimizer and tanh activation func tion. |
| title | Impact of Tuning Parameters in Deep Convolutional Neural Network Using a Crack Image Dataset |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.03184 |