CFP last date
22 April 2024
Reseach Article

Neural Networks and Machine Learning for Pattern Recognition

by Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim A.a. Alqubati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 12
Year of Publication: 2015
Authors: Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim A.a. Alqubati
10.5120/21753-5038

Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim A.a. Alqubati . Neural Networks and Machine Learning for Pattern Recognition. International Journal of Computer Applications. 122, 12 ( July 2015), 29-32. DOI=10.5120/21753-5038

@article{ 10.5120/21753-5038,
author = { Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim A.a. Alqubati },
title = { Neural Networks and Machine Learning for Pattern Recognition },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 12 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number12/21753-5038/ },
doi = { 10.5120/21753-5038 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:23.083988+05:30
%A Arafat A. Muharram
%A Khaled M. G. Noaman
%A Ibrahim A.a. Alqubati
%T Neural Networks and Machine Learning for Pattern Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 12
%P 29-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper represents an application study for using the Neural Networks and Machine Learning to recognize the English alphabet (A-Z) through the use of pattern recognition techniques in image processing and specifically to the application of Neural Networks and machine learning as a matrix two dimension. We used two techniques ANN and ML to compare their efficiencies and accuracies. We got 86. 92% for ANN and 91. 2% for ML.

References
  1. Anil K. Jain, Fundamentals of Digital image Processing, Prentice Hall, USA,1989.
  2. Cosmin Grigorescu, Student Member, IEEE, and Nicolai Petkov: Distance Sets for Shape Filters and Shape Recognition, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003
  3. Dorst, L. and A. W. M. Smeulders, Length estimators compared, in Pattern Recognition in Practice II, E. S. Gelsema and L. N. Kanal, Editors. 1986, Elsevier Science: Amsterdam. p. 73-80.
  4. Gunnar Ratsch, A Brief Introduction into Machine Learning, Friedrich Miescher Laboratory of the Max Planck Society , Germany, www. ccc. de/congress/2004/fahrplan/files
  5. Hirose,Y . ,Back Propagation Algorithm which Variesthe Number of HiddenUnits, NeuralNetworks, Vol. 4,pp. 61-66,1991.
  6. Huang, T. S. , G. J. Yang, and G. Y. Tang, A Fast Two-Dimensional Median Filtering Algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1979. ASSP-27: p. 13-18.
  7. JACKY HERZ and ROGER D. HERSCH: Analyzing character shapes by string matching techniques, Electronic Publishing, VOL. 6(3), 261–272 (SEPTEMBER 1993)
  8. Mickey Williams, David Bennett, et al. , Visual C++6 Copyright 2000 by sams publishing, USA.
  9. . Pavis Chapman, teach yourself Visual C++6 in 21 days. 2000 ,New Delhi
  10. R. Schalkoff ,Artificial Neural Networks, McGraw-Hill,1999.
  11. Russ, J. C. , The Image Processing Handbook. Second ed. 1995, Boca Raton, Florida: CRC .
  12. TOM M. MITCHELL, Machine Learning ,McGraw-Hill, USA,1997
  13. WhiteH,ArtificialNeuralNetworks:ApproximationandLearningTheory,Blackwell,1992.
Index Terms

Computer Science
Information Sciences

Keywords

Neural Networks Machine Learning Image Processing Pattern Recognition.