Understanding of Convolutional Neural Network (CNN): A Review
The 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 pr...
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id-ugm-repo.2826992023-11-17T03:51:40Z https://repository.ugm.ac.id/282699/ Understanding of Convolutional Neural Network (CNN): A Review Purwono, Purwono Ma’arif, Alfian Rahmaniar, Wahyu Fathurrahman, Haris Imam Karim Frisky, Aufaclav Zatu Kusuma Haq, Qazi Mazhar Ul Information and Computing Sciences Distributed Computing The 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. © 2022, Association for Scientific Computing Electronics and Engineering (ASCEE). All rights reserved. 2022 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282699/1/888-2911-2-PB.pdf Purwono, Purwono and Ma’arif, Alfian and Rahmaniar, Wahyu and Fathurrahman, Haris Imam Karim and Frisky, Aufaclav Zatu Kusuma and Haq, Qazi Mazhar Ul (2022) Understanding of Convolutional Neural Network (CNN): A Review. International Journal of Robotics and Control Systems, 2 (4). 739 – 748. https://www.pubs2.ascee.org/index.php/IJRCS/article/view/888/pdf http://dx.doi.org/10.31763/ijrcs.v2i4.888 |
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Information and Computing Sciences Distributed Computing Purwono, Purwono Ma’arif, Alfian Rahmaniar, Wahyu Fathurrahman, Haris Imam Karim Frisky, Aufaclav Zatu Kusuma Haq, Qazi Mazhar Ul Understanding of Convolutional Neural Network (CNN): A Review |
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The 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. © 2022, Association for Scientific Computing Electronics and Engineering (ASCEE). All rights reserved. |
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Purwono, Purwono Ma’arif, Alfian Rahmaniar, Wahyu Fathurrahman, Haris Imam Karim Frisky, Aufaclav Zatu Kusuma Haq, Qazi Mazhar Ul |
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Purwono, Purwono Ma’arif, Alfian Rahmaniar, Wahyu Fathurrahman, Haris Imam Karim Frisky, Aufaclav Zatu Kusuma Haq, Qazi Mazhar Ul |
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Purwono, Purwono |
title |
Understanding of Convolutional Neural Network (CNN): A Review |
title_short |
Understanding of Convolutional Neural Network (CNN): A Review |
title_full |
Understanding of Convolutional Neural Network (CNN): A Review |
title_fullStr |
Understanding of Convolutional Neural Network (CNN): A Review |
title_full_unstemmed |
Understanding of Convolutional Neural Network (CNN): A Review |
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understanding of convolutional neural network (cnn): a review |
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2022 |
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https://repository.ugm.ac.id/282699/1/888-2911-2-PB.pdf https://repository.ugm.ac.id/282699/ https://www.pubs2.ascee.org/index.php/IJRCS/article/view/888/pdf http://dx.doi.org/10.31763/ijrcs.v2i4.888 |
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