Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 5, 25 May 2023


Open Access | Article

The Study for Convolutional Neural Network and Corresponding Applications

Xingchen Chen * 1
1 School of Engineering, University of Edinburgh, City of Edinburgh, the United Kingdom

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 5, 182-187
Published 25 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Xingchen Chen. The Study for Convolutional Neural Network and Corresponding Applications. TNS (2023) Vol. 5: 182-187. DOI: 10.54254/2753-8818/5/20230387.

Abstract

Deep learning is a key technological tool in the field of image identification with wide application prospects because of its significant benefits in feature extraction and model fitting. Deep learning entails numerous stages of non-linear transformations. The primary implementation of the current deep learning technique is the deep neural network, the connection pattern of which takes its reference from the way that the visual cortex of animals is organized. Among all the deep learning methods, Convolutional Neural Network (CNN) is one of the most renowned means to process image. It has excellent performance in terms of large-scale image processing. A convolutional neural network consists of convolutional layers, a fully connected layer, a pooling layer, and associated weights. Convolutional neural networks have fewer parameters to consider than other deep, feed forward neural networks, making them an attractive deep learning architecture. There are several traits in CNN, including pooling, shared weights, and local connections. With the help of these features, the network's complexity and the quantity of training parameters can be decreased, and the model's level of invariance to scale, shift, and distortion can be increased, along with its robustness and fault tolerance. This paper firstly summarized the history of convolutional neural network, then briefly discussed the components like the neuron and multilayer perception. The main structure of the CNN is showed afterwards. The paper also mentioned the features and the applications of CNN, mainly in the field of Computer Vision (CV).

Keywords

Convolutional neural network, Deep learning, Machine learning, Image processing.

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
ISBN (Print)
978-1-915371-53-9
ISBN (Online)
978-1-915371-54-6
Published Date
25 May 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/5/20230387
Copyright
25 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated