University of Bahrain
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Recent Researches on Image Classification Using Deep Learning Approach

Show simple item record Sarkar, Nayan Kumar Singh, Moirangthem Marjit Nandi, Utpal 2022-12-06T20:11:25Z 2022-12-06T20:11:25Z 2022-12-06
dc.identifier.issn 2210-142X
dc.description.abstract Image classification is an essential and widely used area where deep learning is applied. The deep learning approach has been extensively applied in the area of image classification and provides very good classification accuracy. Some of the deep learning approaches can classify images better than a human. Image classification has tremendous applications in practice. This paper presents a survey on some of the deep learning approach-based image classification methods which have been extensively used in various applications of image classifications. The deep learning approaches which have been considered in our study and are used for developing a variety of image classification methods are Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), Generative Adversarial Networks (GAN), Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN). The paper also discusses comparative studies on some of the image classification techniques which have been used in different areas of image classification problems. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Image Classification, CNN, RNN, LSTM, GAN, RBM, DBN en_US
dc.title Recent Researches on Image Classification Using Deep Learning Approach en_US
dc.type Article en_US
dc.volume 12 en_US
dc.issue 1 en_US
dc.pagestart 1357 en_US
dc.pageend 1374 en_US
dc.contributor.authoraffiliation Department of Computer Science and Engineering, North Eastern Regional Institute of Science and Technology, Arunachal Pradesh, India en_US
dc.contributor.authoraffiliation Department of Computer Science, Vidyasagar University, Midnapore, West Bengal, India en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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