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A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification

Image classification is a popular and important area of image processing research in today's society. For machine learning, SVM is a very good classification model. CNN is a type of convolution neural network that has an unpredictable development and uses convolution calculations. It is one of...

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Main Authors: Madanan M., Gunasekaran S.S., Mahmoud M.A.
Other Authors: 57203784027
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:
deep learning
image classification
machine learning
Convolution
Convolutional neural networks
Deep learning
Large datasets
Learning algorithms
Learning systems
Support vector machines
Classification models
Classification procedure
Comparative analyzes
Convolution neural network
Images classification
Images processing
Machine-learning
Small samples
Standard ML
Image classification
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spelling my.uniten.dspace-343742024-10-14T11:19:21Z A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification Madanan M. Gunasekaran S.S. Mahmoud M.A. 57203784027 55652730500 55247787300 deep learning image classification machine learning Convolution Convolutional neural networks Deep learning Large datasets Learning algorithms Learning systems Support vector machines Classification models Classification procedure Comparative analyzes Convolution neural network Deep learning Images classification Images processing Machine-learning Small samples Standard ML Image classification Image classification is a popular and important area of image processing research in today's society. For machine learning, SVM is a very good classification model. CNN is a type of convolution neural network that has an unpredictable development and uses convolution calculations. It is one of the most well-known deep learning algorithms. This review thinks about and inspects exemplary AI and profound learning picture classification procedures involving SVM and CNN as specific illustrations. Using a large sample mnist dataset, this study found that CNN has an accuracy of 0.97 and SVM has an accuracy of 0.89 SVM has an accuracy of 0.85 and CNN has an accuracy of 0.82 when working with a small sample ImageNet dataset. Tests in this review show that for little example informational collections, standard ML has an improved arrangement impact than deep learning structure does. � 2023 IEEE. Final 2024-10-14T03:19:21Z 2024-10-14T03:19:21Z 2023 Conference Paper 10.1109/IC3I59117.2023.10398030 2-s2.0-85187300318 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187300318&doi=10.1109%2fIC3I59117.2023.10398030&partnerID=40&md5=db61abffb0c90ade269079af699993f1 https://irepository.uniten.edu.my/handle/123456789/34374 2436 2439 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic deep learning
image classification
machine learning
Convolution
Convolutional neural networks
Deep learning
Large datasets
Learning algorithms
Learning systems
Support vector machines
Classification models
Classification procedure
Comparative analyzes
Convolution neural network
Deep learning
Images classification
Images processing
Machine-learning
Small samples
Standard ML
Image classification
spellingShingle deep learning
image classification
machine learning
Convolution
Convolutional neural networks
Deep learning
Large datasets
Learning algorithms
Learning systems
Support vector machines
Classification models
Classification procedure
Comparative analyzes
Convolution neural network
Deep learning
Images classification
Images processing
Machine-learning
Small samples
Standard ML
Image classification
Madanan M.
Gunasekaran S.S.
Mahmoud M.A.
A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
description Image classification is a popular and important area of image processing research in today's society. For machine learning, SVM is a very good classification model. CNN is a type of convolution neural network that has an unpredictable development and uses convolution calculations. It is one of the most well-known deep learning algorithms. This review thinks about and inspects exemplary AI and profound learning picture classification procedures involving SVM and CNN as specific illustrations. Using a large sample mnist dataset, this study found that CNN has an accuracy of 0.97 and SVM has an accuracy of 0.89
author2 57203784027
author_facet 57203784027
Madanan M.
Gunasekaran S.S.
Mahmoud M.A.
format Conference Paper
author Madanan M.
Gunasekaran S.S.
Mahmoud M.A.
author_sort Madanan M.
title A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
title_short A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
title_full A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
title_fullStr A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
title_full_unstemmed A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
title_sort comparative analysis of machine learning and deep learning algorithms for image classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061053349199872
score 13.222552

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