Deep Convolutional Neural Network Based Extreme Learning Machine Image Classification

Authors

  • G. D. Praveenkumar  Research Scholar, Computer Science, Kaamadhenu Arts and Science College, sathyamangalam, Tamil Nadu, India
  • Dr. R. Nagaraj  Associate Professor, Computer Science, Kaamadhenu Arts and Science College, sathyamangalam, Tamil Nadu, India

DOI:

https://doi.org/10.32628/IJSRSET1218475

Keywords:

Neural Network, Convolutional Neural Network, Feature Extarction, ELM Classifer, Image Classification.

Abstract

In this paper, we introduce a new deep convolutional neural network based extreme learning machine model for the classification task in order to improve the network's performance. The proposed model has two stages: first, the input images are fed into a convolutional neural network layer to extract deep-learned attributes, and then the input is classified using an ELM classifier. The proposed model achieves good recognition accuracy while reducing computational time on both the MNIST and CIFAR-10 benchmark datasets.

References

  1. Atmane Khellal, Hongbin Ma,Qing, Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ship Recognition in Infrared Images, Sensors,18,1490, 2018.
  2. B.Kaviyarasu, A.V.Senthil Kumar , Web Interaction Mining using Improved Extreme Learning Machine Classifier , International Journal of Research in Science Engineering and Technology, vol.3, no.12, pp. 45-51, 2016.
  3. C.he,H.Kang, T.Yao, X.Li, An effective classifier based on convolutional neural network and regularized ELM, MBE, vol.16, no.6, pp.8309-8321.
  4. CIFAR-10 and CIFAR-100 datasets: https://www.cs.toronto.edu/~kriz/cifar.html.
  5. Enrique S. Marquez, Jonathon S. Hare, and Mahesan Niranjan , Deep Cascade Learning, IEEE Transactions on Neural Networks and Learning Systems, 29 ,pp.5475 – 5485, 2018.
  6. G.D.Praveenkumar, M.Dharmalingam, Pruned Cascade Neural Network Image Classification, International Journal Recent Technology and Engineering.vol 8, pp.6454-6457, 2019.
  7. G.D.Praveenkumar, M.Dharmalingam, Recurrent Cascade Neural Network for Image Classification,International Journal of Scientific and Technology Research. vol.8, pp. 1009-1012, 2019.
  8. Hakan Cevikalp, Burak Benligitay, Omer Nezih Gerek, Semi-Supervised robust deep neural networks for multi-label image classification, Pattern Recognition, vol.100,107164, 2020.
  9. Hoo-Chang Shin et al., Deep CNN for Computer-Aided Detection: CNN Architectures, Dataset characteristics and Transfer Learning ,Arvix, pp.1-14, 2016.
  10. Huang GB, Zhu QY, Siew CK, Extreme learning machine: theory and applications. Neurocomputing, Neural networks selected papers from the 7th Brazilian symposium on neural networks (SBRN ’04), vol.70, no.13, pp.489–501,2006.
  11. Huang GB, Zhu QY, Siew CK, Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the International Joint Conference On Neural Networks, pp.985– 990, 2006.
  12. J.Du et al., Supervised Extreme learning machine auto-encoder for discriminative feature learning, IEEE ,vol 8, pp.11700-11709, 2020.
  13. Jia et al ., A hybrid convolutional neural network with extreme learning machine for WCE image classification, In: Proceeding of IEEE , Robitics and Biomimetrics,2015.
  14. J-S.Han, G-Bae cho, K-C.Kwak, A design of convolutional neural network using ReLU based ELM classifier and its applications, In: Proceeding of ICMLC, Springer, 2017.
  15. L.Li, K.zhoa, S.Li, R.Sun, S.Cai, Extreme learning machine for supervised classification with self paced learning, Neural Processing Letters, Springer, 2020.
  16. M.Malik, N.Roy, ELM-based image classification model using hand written digit database, application of artificial intelligence techniques in engineering, Advances in Intelligent System and Computing, 697.
  17. R.Amalraj, M.Dharmalingam, A Work Point System Coupled with Back-Propagation for Solving Double Dummy Bridge Problem, Neurocomputing. 168,pp.160-178, 2015.
  18. R.Murugan,Tripti Goel, E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional neural network, Journal of ambient intelligence and humanized computing ,2021.
  19. S.Madhusudhanan, S.Jaganathan, Jayashree, Incremental learning for classification of unstructured data using Extreme learning machine, Algorithms,vol.11,158, 2018.
  20. Saqib Ali et al., An effective and improved CNN-ELM for handwritten digit recognition and classification, Symmetry, vol.12, 1742, 2020.
  21. Y.Park,H.S.Yang , Convolutional neural network based on extreme learning machine for image classification, Neurocomputing, 339,pp.66-76, 2019.
  22. Y.Yoo,S.Y.Oh, Fast training of CNN classifier through extreme learning machine, IEEE, pp. 1702-1708, 2016.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2021-10-30

Issue

Volume 8, Issue 5, September-October-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
G. D. Praveenkumar, Dr. R. Nagaraj "Deep Convolutional Neural Network Based Extreme Learning Machine Image Classification" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 5, pp.30-38, September-October-2021. Available at doi : https://doi.org/10.32628/IJSRSET1218475