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Some Studies on Convolution Neural Network

by Goutam Sarker
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 21
Year of Publication: 2018
Authors: Goutam Sarker
10.5120/ijca2018917965

Goutam Sarker . Some Studies on Convolution Neural Network. International Journal of Computer Applications. 182, 21 ( Oct 2018), 13-22. DOI=10.5120/ijca2018917965

@article{ 10.5120/ijca2018917965,
author = { Goutam Sarker },
title = { Some Studies on Convolution Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 182 },
number = { 21 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 13-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number21/30056-2018917965/ },
doi = { 10.5120/ijca2018917965 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:03.254727+05:30
%A Goutam Sarker
%T Some Studies on Convolution Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 21
%P 13-22
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Two major tools to implement any artificial intelligence and machine learning systems are Symbolic AI and Artificial Neural Network AI. Artificial Neural Network (ANN) has made a tremendous improvement in the versatile area of Machine Learning (ML). Artificial Neural Network (ANN) is an assembly of huge number of weighted interconnected artificial neurons, initially invented with the inspiration of biological neurons. All these models are much better than previous models implemented with symbolic AI so far as their performance is concerned. One revolutionary change in ANN is Convolutional Neural Network (CNN). These structures are mainly suitable for complex pattern recognition tasks within images for the purpose of computer vision.

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Index Terms

Computer Science
Information Sciences

Keywords

Convolution Neural Network Deep Learning Optical Character Recognition Machine Transcription Machine Translation Accuracy

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