Deep Learning Analysis: A Review

Back to Journal

Home»Articles»Deep Learning Analysis: A Review

Author : P. Rajendra Kumar, Suban Ravichandran and Narayana Satyala
Volume 7 No.1 Special Issue:November 2018 pp 24-28

Abstract

Deep learning is a rising territory of machine learning (ML) inquires about. It includes different shrouded layers of fake neural systems. Deep learning (DL) is a part of machine learning dependent on an arrangement of calculations that endeavor to show abnormal state reflections in information. It is utilized by Google in its voice and picture acknowledgment calculations, by Netflix and Amazon to choose what you need to watch or purchase straightaway, and by specialists at MIT to anticipate what’s to come. Profound Learning is utilized in different fields for accomplishing various levels of deliberation like sound, content; pictures highlight extraction and so forth. The Deep learning philosophy applies nonlinear changes and model reflections of abnormal state in extensive databases. With Deep learning capacity to make forecasts and groupings taking the upside of huge information, it can be a creative answer for issues and issues that have been never thought to be understood in such a simple way. Then again, it makes numerous difficulties on the researchers who are endeavoring to convey such another methodology. The accompanying audit sequentially shows how and in what real applications profound realizing calculations have been used. We have completed a broad writing audit and reviewed the utilization of profound learning methods on different fields.

Keywords

Deep Learning, Machine Learning, Reinforcement Learning, Speech Recognition, and Image Processing

Full Text:

References

[1] O. Abdel, “Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition”, Acoustics, Speech and Signal Processing, Vol. 7, pp. 4277-4280, 2012.
[2] A. Mosavi and A. Varkonyi-Koczy, “Integration of machine learning and optimization for robot learning”, Advances in Intelligent Systems and Computing, Vol. 519, pp. 349-355, 2017.
[3] A. Mosavi, A. Varkonyi-Koczy and M. Fullsack, “Combination of machine learning and optimization for automated decision-Making”, in International Conference on Multiple Criteria Decision Making, 2015.
[4] A. Coates,H. Lee and Y. Ng. Andrew, “An analysis of single- layer networks in unsupervised feature learning”, in International Conference on Artificial Intelligence and Statistics, 2011.
[5] Li. Deng and Yu. Dong, “Deep learning: methods and applications”, Foundations and Trends® in Signal Processing, Vol. 7, No. 3-4, pp.197-387, 2014.
[6] A. Mohamed, “Deep belief networks for phone recognition”, Nips Workshop on Deep Learning for Speech Recognition and Related Applications, Vol. 1, pp. 635-645, 2009.
[7] A. Mosavi and A. Vaezipour, “Reactive search optimization; application to multi objective optimization problems”, Applied Mathematics, Vol. 3, pp.1572-1582, 2012.
[8] B. Goel, “Developments in the field of natural language processing”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 3, pp. 23-28, 2017.
[9] T.S. Lee and D. Mumford, “Hierarchical Bayesian inference in the visual cortex”, in Journal of the Optical Society of America, Vol. 20, No. 7,pp.1434-1448, 2003.
[10] Y. Le Cun, Y. Bengio and Geoffrey Hinton, “Deep learning”, Nature, pp. 436-444, 2015.
[11] A. Rushton, “Formative assessment: a key to deep learning?” 2007.
[12] P. Kiran Sree, “A fast multiple attractor cellular automata with modified clonal classifier for coding region prediction in human genome”, Journal of Bioinformatics and Intelligent Control, Vol. 3, pp.1-6, 2014.
[13] P. Baldi, “Deep Learning in Biomedical Data Science”, 2018.
[14] P. KiranSree, “Investigating an artificial immune system to strengthen the protein structure prediction and protein coding region identification using cellular automata classifier”, International Journal of Bioinformatics Research and Applications ,Vol. 5, No. 6,pp. 647-662, 2009.
[15] P. KiranSree, “Identification of promoter region in genomic DNA using cellular automata based text clustering”, The International Arab Journal of Information Technology, Vol.7, No.1, pp.75-78, 2010.
[16] David H. Hubel and Torsten N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex”, in Journal of Physiology, Vol. 160, No. 1, pp.106–154, 1962.
[17] S. Safdar, S. Zafar, N. Zafar and N.F. Khan, “Machine learning based decision support systems (DSS) for heart disease diagnosis: a review”, Artificial Intelligence Review, pp. 1-17, 2017.
[18] Vaezipour, A. Mosavi and U. Seigerroth, “Visual analytics and informed decisions in health and life sciences”, in International CAE Conference, Verona, Italy, 2013.
[19] W. Liu, “Deep learning hashing for mobile visual search”, in EURASIP Journal on Image and Video Processing, 2017.
[20] R. Salakhutdinov and Geoffrey Hinton, “Deep boltzmann machines”, in Artificial Intelligence and Statistics, pp.448-455, 2009.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

Foundations of High-performance ComputingTheory of algorithms and computability

Parallel & distributed computing

Computer networks

Neural networks

LAN/WAN/MAN

Database theory & practice

Mobile Computing for e-Commerce

Future Internet architecture

Protocols and services

Mobile and ubiquitous networks

Green networking

Internet content search

Opportunistic networking

Network applications

Network scaling and limits

Artifial Intelligences

Pattern/Image Recognitions

Communication Network

Information Security

Knowledge Management

Management Information systems

Multimedia communicatiions

Operations research

Optical networks

Software Engineering

Virtual reality

Web Technologies

Wireless technology

Deep learning is a rising territory of machine learning (ML) inquires about. It includes different shrouded layers of fake neural systems. Deep learning (DL) is a part of machine learning dependent on an arrangement of calculations that endeavor to show abnormal state reflections in information. It is utilized by Google in its voice and picture acknowledgment calculations, by Netflix and Amazon to choose what you need to watch or purchase straightaway, and by specialists at MIT to anticipate what's to come. Profound Learning is utilized in different fields for accomplishing various levels of deliberation like sound, content; pictures highlight extraction and so forth. The Deep learning philosophy applies nonlinear changes and model reflections of abnormal state in extensive databases. With Deep learning capacity to make forecasts and groupings taking the upside of huge information, it can be a creative answer for issues and issues that have been never thought to be understood in such a simple way. Then again, it makes numerous difficulties on the researchers who are endeavoring to convey such another methodology. The accompanying audit sequentially shows how and in what real applications profound realizing calculations have been used. We have completed a broad writing audit and reviewed the utilization of profound learning methods on different fields.