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Improvement in Convolutional Neural Network for CIFAR-10 Dataset Image Classification

by Suyesh Pandit, Sushil Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 37
Year of Publication: 2020
Authors: Suyesh Pandit, Sushil Kumar

Suyesh Pandit, Sushil Kumar . Improvement in Convolutional Neural Network for CIFAR-10 Dataset Image Classification. International Journal of Computer Applications. 176, 37 ( Jul 2020), 25-29. DOI=10.5120/ijca2020920489

@article{ 10.5120/ijca2020920489,
author = { Suyesh Pandit, Sushil Kumar },
title = { Improvement in Convolutional Neural Network for CIFAR-10 Dataset Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 37 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2020920489 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:41:17.742196+05:30
%A Suyesh Pandit
%A Sushil Kumar
%T Improvement in Convolutional Neural Network for CIFAR-10 Dataset Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 37
%P 25-29
%D 2020
%I Foundation of Computer Science (FCS), NY, USA

Image classification requires the generation of features capable of detecting image patterns informative of group identity. The objective of this study was to classify images from the public CIFAR10 image dataset by leveraging combinations of disparate image feature sources from deep learning approaches. The majority of regular convolutional neural networks (CNN) are based on the same structure: modification of convolution and the process of max-pooling layers connected with a number of entirely linked layers. In this paper, the prime objective is to improve the effectiveness of simple convolutional neural network models. The Artificial Neural Network (ANN) algorithm is applied on a Canadian Institute For Advanced Research dataset (CIFAR-10) using two different CNN structures. The result of the improved model achieves 88% classification accuracy rate by running for 10 hours. The deep learning models are implemented with the use of Keras library available for Python programming language.

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Index Terms

Computer Science
Information Sciences


Artificial neural networks cifar-10 classification image convolutional neural networks keras python jupyter note book machine learning