A systematic review on deep learning architectures and applications

The amount of digital data in the universe is growing at an exponential rate, doubling every 2 years, and changing how we live in the world. The information storage capacity and data requirement crossed the zettabytes. With this level of bombardment of data on machine learning techniques, it becomes...

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Bibliographic Details
Published in:Expert systems Vol. 36; no. 3
Main Authors: Khamparia, Aditya, Singh, Karan Mehtab
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.06.2019
Subjects:
Artificial intelligence
Artificial neural networks
autoencoders
Belief networks
Bombardment
convolutional neural networks
Deep learning
deep networks
Digital data
Feature extraction
Information storage
Machine learning
Natural language processing
restricted Boltzmann's machine
Scientific papers
stacked autoencoders
Storage capacity
tensor deep stack networks
Universe
ISSN:0266-4720, 1468-0394
Online Access:Get full text
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Summary:The amount of digital data in the universe is growing at an exponential rate, doubling every 2 years, and changing how we live in the world. The information storage capacity and data requirement crossed the zettabytes. With this level of bombardment of data on machine learning techniques, it becomes very difficult to carry out parallel computations. Deep learning is broadening its scope and gaining more popularity in natural language processing, feature extraction and visualization, and almost in every machine learning trend. The purpose of this study is to provide a brief review of deep learning architectures and their working. Research papers and proceedings of conferences from various authentic resources (Institute of Electrical and Electronics Engineers, Wiley, Nature, and Elsevier) are studied and analyzed. Different architectures and their effectiveness to solve domain specific problems are evaluated. Various limitations and open problems of current architectures are discussed to provide better insights to help researchers and student to resume their research on these issues. One hundred one articles were reviewed for this meta‐analysis of deep learning. From this analysis, it is concluded that advanced deep learning architectures are combinations of few conventional architectures. For example, deep belief network and convolutional neural network are used to build convolutional deep belief network, which has higher capabilities than the parent architectures. These combined architectures are more robust to explore the problem space and thus can be the answer to build a general‐purpose architecture.
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ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.12400