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Deep learning for image processing in WEKE environment

Deep learning is a new term that is recently popular among researchers when dealing with big data such as images, texts, voices and other types of data. Deep learning has become a popular algorithm for image processing since the last few years due to its better performance in visualizing and classif...

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Main Authors:	Zainudin, Z., Shamsuddin, S. M., Hasan, S.
Format:	Article
Language:	English
Published:	International Center for Scientific Research and Studies 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/90130/1/ZanariahZainudin2019_DeepLearningforImageProcessing.pdf http://eprints.utm.my/id/eprint/90130/ http://home.ijasca.com/data/documents/1_page1-21_Deep-Learning-for-Image-Processing-in-WEKA-Environment_1.pdf.
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id	my.utm.90130
record_format	eprints
spelling	my.utm.901302021-03-29T06:00:12Z http://eprints.utm.my/id/eprint/90130/ Deep learning for image processing in WEKE environment Zainudin, Z. Shamsuddin, S. M. Hasan, S. QA75 Electronic computers. Computer science Deep learning is a new term that is recently popular among researchers when dealing with big data such as images, texts, voices and other types of data. Deep learning has become a popular algorithm for image processing since the last few years due to its better performance in visualizing and classifying images. Nowadays, most of the image datasets are becoming larger in terms of size and variety of the images that can lead to misclassification due to human eyes. This problem can be handled by using deep learning compared to other machine learning algorithms. There are many open sources of deep learning tools available and Waikato Environment for Knowledge Analysis (WEKA) is one of the sources which has deep learning package to conduct image classification, which is known as WEKA DeepLearning4j. In this paper, we demonstrate the systematic methodology of using WEKA DeepLearning4j for image classification on larger datasets. We hope this paper could provide better guidance in exploring WEKA deep learning for image classification. International Center for Scientific Research and Studies 2019 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90130/1/ZanariahZainudin2019_DeepLearningforImageProcessing.pdf Zainudin, Z. and Shamsuddin, S. M. and Hasan, S. (2019) Deep learning for image processing in WEKE environment. International Journal of Advances in Soft Computing and its Applications, 11 (1). pp. 1-21. ISSN 2074-8523 http://home.ijasca.com/data/documents/1_page1-21_Deep-Learning-for-Image-Processing-in-WEKA-Environment_1.pdf.
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Zainudin, Z. Shamsuddin, S. M. Hasan, S. Deep learning for image processing in WEKE environment
description	Deep learning is a new term that is recently popular among researchers when dealing with big data such as images, texts, voices and other types of data. Deep learning has become a popular algorithm for image processing since the last few years due to its better performance in visualizing and classifying images. Nowadays, most of the image datasets are becoming larger in terms of size and variety of the images that can lead to misclassification due to human eyes. This problem can be handled by using deep learning compared to other machine learning algorithms. There are many open sources of deep learning tools available and Waikato Environment for Knowledge Analysis (WEKA) is one of the sources which has deep learning package to conduct image classification, which is known as WEKA DeepLearning4j. In this paper, we demonstrate the systematic methodology of using WEKA DeepLearning4j for image classification on larger datasets. We hope this paper could provide better guidance in exploring WEKA deep learning for image classification.
format	Article
author	Zainudin, Z. Shamsuddin, S. M. Hasan, S.
author_facet	Zainudin, Z. Shamsuddin, S. M. Hasan, S.
author_sort	Zainudin, Z.
title	Deep learning for image processing in WEKE environment
title_short	Deep learning for image processing in WEKE environment
title_full	Deep learning for image processing in WEKE environment
title_fullStr	Deep learning for image processing in WEKE environment
title_full_unstemmed	Deep learning for image processing in WEKE environment
title_sort	deep learning for image processing in weke environment
publisher	International Center for Scientific Research and Studies
publishDate	2019
url	http://eprints.utm.my/id/eprint/90130/1/ZanariahZainudin2019_DeepLearningforImageProcessing.pdf http://eprints.utm.my/id/eprint/90130/ http://home.ijasca.com/data/documents/1_page1-21_Deep-Learning-for-Image-Processing-in-WEKA-Environment_1.pdf.
_version_	1696976265743958016
score	13.211869

Deep learning for image processing in WEKE environment

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