Deep Convolutional Neural Networks based Galaxies Classification

Authors

  • Sanket Kulkarni  Computer Engineering,SPPU/SKN SITS, lonavala, Maharashtra, India
  • Kaustubh Deshmukh  Computer Engineering,SPPU/SKN SITS, lonavala, Maharashtra, India
  • Nikhil Lad  Computer Engineering,SPPU/SKN SITS, lonavala, Maharashtra, India
  • Dinesh Inamdar  Computer Engineering,SPPU/SKN SITS, lonavala, Maharashtra, India

Keywords:

Galaxies Classification, Deep Convolutional Neural Networks, Computational Astrophysics

Abstract

In this Project, The neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into a main three categories Elliptical, Spiral, and Irregular. This paper presents an new approach for an automatic detection of galaxy morphology from datasets based on the image-retrieval approach. The galaxy can be classified based on its features into a main three categories Elliptical, Spiral, and Irregular.

References

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  2. M. Abd Elfattah, N. Elbendary, H. K. Elminir, M. A. Abu El-Soud, and A.E. Hassanien, Galaxies image classification using empirical mode decomposition and machine learning techniques, in 2014 International Conference on Engineering and Technology (ICET), 2014, pp. 15.
  3. A. Adams and A. Woolley, Hubble classification of galaxies using neural networks, Vistas Astron., vol. 38, pp. 273280, Jan. 1994.
  4. A. Dominguez, A History of the Convolution Operation Retrospectroscope], IEEE Pulse, vol. 6, no. 1, pp. 3849, Jan. 2015.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-04-06

Issue

Volume 5, Issue 7, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Sanket Kulkarni, Kaustubh Deshmukh, Nikhil Lad, Dinesh Inamdar, " Deep Convolutional Neural Networks based Galaxies Classification, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 5, Issue 7, pp.108-110, March-April-2019.