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Some Studies on Convolution Neural Network

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Goutam Sarker
10.5120/ijca2018917965

Goutam Sarker. Some Studies on Convolution Neural Network. International Journal of Computer Applications 182(21):13-22, October 2018. BibTeX

@article{10.5120/ijca2018917965,
	author = {Goutam Sarker},
	title = {Some Studies on Convolution Neural Network},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2018},
	volume = {182},
	number = {21},
	month = {Oct},
	year = {2018},
	issn = {0975-8887},
	pages = {13-22},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume182/number21/30056-2018917965},
	doi = {10.5120/ijca2018917965},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Two major tools to implement any artificial intelligence and machine learning systems are Symbolic AI and Artificial Neural Network AI. Artificial Neural Network (ANN) has made a tremendous improvement in the versatile area of Machine Learning (ML). Artificial Neural Network (ANN) is an assembly of huge number of weighted interconnected artificial neurons, initially invented with the inspiration of biological neurons. All these models are much better than previous models implemented with symbolic AI so far as their performance is concerned. One revolutionary change in ANN is Convolutional Neural Network (CNN). These structures are mainly suitable for complex pattern recognition tasks within images for the purpose of computer vision.

References

  1. Ian Goodfellow and Yoshua Bengio and Aaron Courville: Deep Learning, MIT Press.
  2. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, H.: Convolutional neural network committees for handwritten character classification. In:Document Analysis and Recognition (ICDAR), 2011 International Conference on. Pp 1135-1139, IEEE(2011).
  3. Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., Culurciello, E.: Hardware accelerated convolutional neural networks for synthetic vision systems. In:Circuits and Systems (ISCAS). Proceedings of 2010 IEEE International Symposium on. Pp 257-260. IEEE (2010)
  4. Karpathy, A. Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large scale video classification with convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. Pp. 17ng,
  5. T., Sukthankar, R., Fei-Fei, L.: Large scale video classification with convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. Pp. 1725-1732. IEEE(2014).
  6. Nebaaer, C.: Evaluation of convolutional neural networks for visual recognition. Neural Networks, IEEE Transactions on 9(4), 685-696 (1998).
  7. Simard, P.Y., Steinkraus, D., Platt, J,C.: Best practices for convolutional neural networks applied to visual document analysis. In: null. P. 958. IEEE(2003).
  8. Szarvas, M., Yoshizawa, A., Yamamoto,M., Ogata, J.:Pedestrain detection with convolutional neural networks. In: Intelligent Vehicles Symposium, 2005. Proceedings. IEEE. Pp. 224-229. IEEE(2005).
  9. Szegedy, C., Toshev, A., Erhan, D. : Deep neural networks for object detection. In: Advances in Neural Information Processing Systems. Pp. 2553-2561 (2013).
  10. Tivive, F.H.C., Bouzerdoum, A. : A new class of convolutional neural networks (siconnets) and their applications of face detection. In: Neural Networks, 2003. Proceedings of the International Joint Conference on. Vol. 3, pp 2157-2162. IEEE(2003).
  11. Zeiler, M.D., Fergus, R.:Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv: 1301.3557(2013).
  12. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Computer Vision – ECCV 2014, pp. 818-833. Springer (2014)
  13. Sarker, G.(2000),A Learning Expert System for Image Recognition, Journal of The Institution of Engineers (I), Computer Engineering Division.,Vol. 81, 6-15.
  14. Mehak and Tarun Gulati, Detection of Digital Forgery Image using Different Techniques, International Journal of Engineering Trends and Technology (IJETT) – Volume 46 Number 8 April 2017.
  15. G. Sarker(2010),A Probabilistic Framework of Face Detection , International Journal of Computer, Information Technology and Engineering (IJCITAE),4(1), 19-25.
  16. G. Sarker(2011),A Multilayer Network for Face Detection and Localization, International Journal of Computer, Information Technology and Engineering (IJCITAE), 5(2), 35-39.
  17. G. Sarker(2012),A Back Propagation Network for Face Identification and Localization, International Journal of Computer, Information Technology and Engineering (IJCITAE),6(1), 1-7.
  18. G. Sarker(2012), An Unsupervised Learning Network for Face Identification and Localization, International Journal of Computer, Information Technology and Engineering (IJCITAE),6(2), 83-89.
  19. G. Sarker and K. Roy (2013), A Modified RBF Network With Optimal Clustering For Face Identification and Localization, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106.,1(3), 30 -35.
  20. G. Sarker and K. Roy(2013), An RBF Network with Optimal Clustering for Face Identification, Engineering Science International Research Journal (ISSN) – 2300 – 4338 ,1(1),ISBB: 978-93-81583-90-6, 70-74.
  21. G. Sarker(2013), An Optimal Back Propagation Network for Face Identification and Localization, International Journal of Computers and Applications (IJCA),ACTA Press, Canada.,35(2).,DOI 10.2316 / Journal .202.2013.2.202 – 3388.
  22. 22. G. Sarker and S. Sharma (2014), A Heuristic Based RBFN for Location and Rotation Invariant Clear and Occluded Face Identification, International Journal of Computer Information Technology and Engineering (IJCITAE), Serials Publications ,8(2),109-118.
  23. G. Sarker(2014), A Competitive Learning Network for Face Detection and Localization, International Journal of Computer Information Technology and Engineering (IJCITAE), Serials Publications, 8(2),119-123.
  24. G. Sarker(2002), A Semantic Concept Model Approach for Pattern Classification and recognition, 28th Annual Convention and Exhibition IEEE – ACE 2002.,December 20-21 2002 , Science City ,Kolkata, 271 – 274.
  25. G. Sarker(2005), A Heuristic Based Hybrid Clustering for Natural Classification in Data Mining, 20th Indian Engineering Congress, organized by The Institution of Engineers (India), December 15-18, 2005, Kolkata, INDIA, paper no. 4.
  26. G. Sarker(2011), A Back propagation Network for Face Identification and Localization, 2011 International Conference on Recent Trends in Information Systems (ReTIS–2011) held in Dec. 21-23, Kolkata, DOI: 10.1109/ReTIS.2011.6146834, pp 24-29.
  27. G. Sarker(2012), An Unsupervised Learning Network for Face Identification and Localization, 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS) Dec. 28 and 29, 2012, Kolkata, DOI: 10.1109/CODIS.2012.6422282, pp 652- 655.
  28. G. Sarker, and K. Roy(2013), An RBF Network with Optimal Clustering for Face Identification, International Conference on Information & Engineering Science – 2013(ICIES -2013), Feb. 21-23 2013, organized by IMRF, Vijayawada, Andhra Pradesh, pp – 70-74.
  29. G. Sarker and K. Roy(2013), A Modified RBF Network with Optimal Clustering for Face Identification and Localization, International Conference on Information & Engineering Science – 2013(ICIES -2013), Feb. 21-23 2013, organized by IMRF, Vijayawada, Andhra Pradesh pp 32-37.
  30. K. Roy and G. Sarker (2013), A Location Invariant Face Identification and Localization with Modified RBF Network, International Conference on Computer and Systems ICCS-2013, 21-22 September, 2013, pp – 23-28, Bardhaman.
  31. G. Sarker and S. Sharma(2014), A Heuristic Based RBFN for Location and Rotation Invariant Clear and Occluded Face Identification, International Conference on Advances in Computer Engineering and Applications, ICACEA – 2014, with IJCA), pp – 30-36.

Keywords

Convolution Neural Network, Deep Learning, Optical Character Recognition, Machine Transcription, Machine Translation, Accuracy