We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Pattern Recognition Approaches inspired by Artificial Immune System

by Aanchal Malhotra, Abhishek Baheti, Shilpi Gupta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 20
Year of Publication: 2012
Authors: Aanchal Malhotra, Abhishek Baheti, Shilpi Gupta
10.5120/6378-8820

Aanchal Malhotra, Abhishek Baheti, Shilpi Gupta . Pattern Recognition Approaches inspired by Artificial Immune System. International Journal of Computer Applications. 44, 20 ( April 2012), 12-16. DOI=10.5120/6378-8820

@article{ 10.5120/6378-8820,
author = { Aanchal Malhotra, Abhishek Baheti, Shilpi Gupta },
title = { Pattern Recognition Approaches inspired by Artificial Immune System },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 20 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number20/6378-8820/ },
doi = { 10.5120/6378-8820 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:03.912306+05:30
%A Aanchal Malhotra
%A Abhishek Baheti
%A Shilpi Gupta
%T Pattern Recognition Approaches inspired by Artificial Immune System
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 20
%P 12-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we have presented a survey on Pattern Recognition technique using a new computational paradigm of Artificial Immune System. Inspired by the biological immune system, it aims to provide solutions for problems in a vast range of domains using novel computational tools. The main use of AIS in pattern recognition is in the field of data mining. The basic immune theories used to explain how the immune system performs pattern recognition are described and their corresponding computational models are presented. We also present a survey on the applications of AIS in various fields related to pattern recognition.

References
  1. Forrest, S. , Somayaji, A & Ackley, D. H. 1997. Building Diversity Computer Systems. In proceedings of the 6th workshop on Hot Topics in Operating Systems, 66-72.
  2. Ayara, M. , Timmis, J. , De Lemos, R. , De Castro, L. , Duncan, R. 2002. Negative Selection: How to Generate Detectors. In proceedings of 1st ICARIS.
  3. Igawa K. and Ohashi H. 2008. A negative selection algorithm for classification and reduction of the noise effect. Applied Soft Computing. Vol. 9(1), 431-438.
  4. Gonzalez L. and Cannady J. 2004. A Self-Adaptive Negative Selection Approach for Anomaly Detection. Congress on Evolutionary Computation. Vol. 2, 1561-1568.
  5. Zeng J. , Li T. , Liu X. , Liu C. , Peng L. and Sun F. 2007. A Feedback Negative Selection Algorithm to Anomaly Detection. Third International Conference on Natural Computation. Vol. 3 (Aug 2007), 604-608.
  6. Xia F. , Zhu Y. , and Gao Y. 2007. Shape-space based negative selection algorithm and its application on power transformer fault diagnosis. In Proceedings of the IEEE International Conference on Robotics and Biomimetics, (Dec 2007), 2149 - 2154.
  7. Zhengbing H. , Ji Z. , and Ping M. 2008. A Novel Anomaly Detection Algorithm Based on Real-Valued Negative Selection System. Workshop on Knowledge Discovery and Data Mining, (Jan 2008), 499-502.
  8. De Castro, L. N. & Von Zuben, F. J. 2000. The Clonal Selection Algorithm with Engineering Applications. In proceedings of GECCO'00, (July 2000), 36-37.
  9. L. N. De Castro & F. J. Von Zuben. 2001. aiNet: An Artificial Immune Network for Data Analysis. In Data Mining: A Heuristic Approach, chapter 12, USA: Idea Group Publishing, pp. 231–259.
  10. Timmis, J. 2000. Artificial Immune Systems: A novel data analysis technique inspired by the immune network theory. Ph. D. Dissertation, Dept. of Computer Science, University of Wales. Available from: link (Assessed on: April 26, 2012).
  11. Timmis, J. , Neal, M. , Hunt, J. 2000. An artificial immune system for data analysis. Biosystems. Vol. 55(1-3), 143-150.
  12. Knight, T and Timmis, J. 2001. AINE: An immunological approach to data mining. In Proceedings of IEEE ICDM. ISBN: 0-7695-1119-8.
  13. De Castro, L. N. , Von Zuben, F. J. , Deus, G. A. The Construction of a Boolean Competitive Neural Network Using Ideas from Immunology. Neurocomputing. Vol. 50(Jan 2003), 51-85.
  14. De Castro, L. N, Timmis, J. 2002. Artificial immune systems: a novel paradigm for pattern recognition. In Artificial Neural Networks in Pattern Recognition. University of Paisley, UK, pp. 67-84, 2002.
  15. De Castro, L. N, Timmis, J. 2002. Artificial Immune Systems: A New Computational Intelligence Approach. Berlin, Germany: Springer-Verlag.
  16. Timmis, J, Neal, M. 2001. A resource limited artificial immune system for data analysis. Knowledge-Based Systems. Vol. 14(324), 121-130.
  17. Sumathi, S. , Paneerselvam, S. 2010. Computational Intelligence Paradigms Theory & Applications using MATLAB. CRC Press 1 edition.
  18. Al-Enezi, J. R. , Abbod, M. F. and Alsharhan, S. Artificial immune system- models, algorithms and applications. International Journal of Research and Reviews in Applied Sciences (IJRRAS). Vol. 2, 118-131.
  19. Nossal, G. J. V. 1993. The Molecular and Cellular Basis of Affinity Maturation in the Antibody Response. Cell. Vol. 68(1), 1-2.
  20. Langman, R. E. & Cohn, M. 1986. The 'complete' idiotype network is an absurd immune system. Immunology Today. Vol. 7(4), 100-101.
  21. Jerne, N. K. 1974. Towards a Network Theory of the Immune System. Ann Immunol (Inst. Pasteur) 125C, pp. 373-389.
  22. Forrest, S. , Perleson, A. , Allen, L. , and Cherukuri, R. 1994. Self-Nonself discrimination in a computer. In Proceedings of IEEE Computer Security Symposium on Research in Security and Privacy. May 1994, 202–212.
  23. Kim J. , Bentley P. , Aickelin U. , Greensmith J. , Tedesco G. , Twycross J. 2007. Immune system approaches to intrusion detection—a review. NATURAL COMPUTING. Vol. 6(4), 413-466.
  24. Wang H. , Peng D. , Wang W. , Sharif H. , Wegiel J. , Nguyen D. , Bowne R. , Backhaus C. Artificial Immune System based image pattern recognition in energy efficient Wireless Multimedia Sensor Networks. Military Communications Conference, MILCOM 2008. IEEE, 1–7.
  25. De Castro, L. N. , and Timmis, J. 2002. An artificial immune network for multimodal optimization. In proceedings of the 2002 Congress on Evolutionary Computation. Vol. 1, 699–704.
  26. Bradly, D. W. & Tyrrell, A. M. 2000. Immunotronics: Hardware Fault Tolerance Inspired by the Immune System. Lecture Notes in Computer Science, 1801, pp. 11-20.
Index Terms

Computer Science
Information Sciences

Keywords

Pattern Recognition Adaptive Immunity Artificial Immune System Negative Selection Clonal Selection Immune Networks

Powered by PhDFocusTM