CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Cancer Classification using Adaptive Neuro Fuzzy Inference System with Runge Kutta Learning

by C. Loganathan, K. V. Girija
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 4
Year of Publication: 2013
Authors: C. Loganathan, K. V. Girija
10.5120/13733-1530

C. Loganathan, K. V. Girija . Cancer Classification using Adaptive Neuro Fuzzy Inference System with Runge Kutta Learning. International Journal of Computer Applications. 79, 4 ( October 2013), 46-50. DOI=10.5120/13733-1530

@article{ 10.5120/13733-1530,
author = { C. Loganathan, K. V. Girija },
title = { Cancer Classification using Adaptive Neuro Fuzzy Inference System with Runge Kutta Learning },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 4 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 46-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number4/13733-1530/ },
doi = { 10.5120/13733-1530 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:52:11.269348+05:30
%A C. Loganathan
%A K. V. Girija
%T Cancer Classification using Adaptive Neuro Fuzzy Inference System with Runge Kutta Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 4
%P 46-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cancer research is one of the major research areas in the medical ?eld. Adaptive Neuro Fuzzy Interference System is used for the classification of Cancer. This algorithm compared with proposed algorithm of Adaptive Neuro Fuzzy Interference system with Runge Kutta learning method for the best classification of cancer. It is one of the better techniques for the classification of the cancer. The Adaptive Network-based Fuzzy Inference System is one of the well-known neural fuzzy controllers with fuzzy inference capability. For the cancer classification inputs are collected from the dataset of Lymphoma dataset and Leukemia dataset. In this paper, focused in classification of cancer by using ANFIS with RKLM.

References
  1. A. Alizadeh, M. B. Eisen, R. E. Davis, C. Ma, I. S. Lossos, A. Rosenwald, J. C. Boldrick, H. Sabet, T. Tran, X. Yu, et al. , "Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling," Nature, vol. 403, pp. 503–511, 2000.
  2. S. Y. Belal, A. F. G. Taktak, A. J. Nevill, S. A. Spencer, D. Roden, S. Bevan, "Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system," Artificial Intelligence in Medicine, vol. 24, pp. 149-165, 2002.
  3. Chen Liao and Shutao Li, "A support vector machine ensemble for cancer classification using gene expression data", ISBRA'07: Proceedings of the 3rd international conference on Bioinformatics research and applications, 2007.
  4. J. Deutsch, "Evolutionary algorithms for finding optimal gene sets in microarray prediction," Bioinformatics, vol. 19, pp. 45–52, 2003.
  5. Dudoit, J. Fridlyand, and T. P. Speed, "Comparison of discrimination methods for the classification of tumors using gene expression data," Journal of the American Statistical Association, vol. 97, pp. 77–87, 2002.
  6. Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik. Gene Selection for Cancer Classification using Support Vector Machines", Machine Learning, Vol. 46, No. 1-3 pp. 389-422. 2002. .
  7. Jamal M. Nazzal, Ibrahim M. El-Emary and Salam A. Najim, "Multilayer Perceptron Neural Network (MLPs) For Analyzing the Properties of Jordan Oil Shale", World Applied Sciences Journal 5.
  8. Jang. J. S. R and Sun. C. T, Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice-Hall, Inc. , Upper Saddle River, NJ, USA, 1997.
  9. J. -S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23(3), pp. 665-685, 1993.
  10. J. M. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, et al. , "Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks," Nature Medicine, vol. 7, pp. 673–679, 2001.
  11. C. . Loganathan and K. V. Girija "A New Hybrid Learning for Adaptive Neuro Fuzzy Inference System" International Journal of Computer Science And Technology www. ijcst . com IJCST Vol. 4, Issue 3, July - Sept 2013 .
  12. Mingjun Song and Sanguthevar Rajasekaran, "A greedy algorithm for gene selection based on SVM and correlation" International journal of Bioinformatics Research and Applications, 2010.
  13. R . Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, "Class predicition by nearest shrunken centroids with applications to DNA microarrays," Statistical Science, vol. 18, pp. 104–117, 2003.
  14. R. Tibshirani, T. Hastie, B. Narashiman, and G. Chu, "Diagnosis of multiple cancer types by shrunken centroids of gene expression," Proc. Natl. Acad. Sci. USA, vol. 99, pp. 6567–6572, 2002.
  15. O. Troyanskaya, M. Cantor, G. Sherlock, et al. , "Missing value stimation methods for DNA microarrays," Bioinformatics, vol. 17, pp. 520–525, 2001.
  16. I. Virant-Klun, J. Virant, "Fuzzy logic alternative for analysis in the biomedical sciences," Computers and Biomedical Research, vol. 32, pp. 305-321, 1999.
  17. Wang, Y-J. , C-T. Lin, "Runge-Kutta Neural Network for Identification of Dynamical Systems in High Accuracy", IEEE Transactions on Neural Networks, Vol. 9. , No. 2, pp. 294-307, March 1998.
  18. http://en. wikipedia. org/wiki/Stochastic_gradient_descent,29th January 2012.
Index Terms

Computer Science
Information Sciences

Keywords

ANFIS RKLM ANFIS with RKLM.

Powered by PhDFocusTM