Abstract
Knowledge mining from large voluminous data, i.e., big data is a difficult and important task nowadays; new techniques are required to be implemented in the field of data sciences, which helps in decision making and implemented in computer science research and development such as artificial intelligence, database, statistics, visualization, and high-performance parallel computing. An artificial immune system includes various algorithms inspired by the biological immune system. These algorithms support machine learning and very useful in solving complex problems such as intrusion detection, anomaly detection, and prevention, data clustering, classification, and exploration. The proposed method provides the comparative study of various supervised learning algorithms of artificial immune recognition system and artificial neural network for classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Watkins A, Lois B (2002) A new classifier based on resource limited artificial immune system ‘IEEE 0-7803-7282-4 02
Brownlee J (2005) Artificial Immune recognition system (AIRS) a review and analysis
http://osp.mans.edu.eg/rehan/ann/2_2%20Biological%20Neural%20Networks.htm
Arbib MA (1987) Brains, machines, and mathematics (2nd edn). Springer, New York, NY
Dasgupta D, Nino LF (2009) Immunological computation theory and application. CRC Press
Dasgupta D, Ji Gonzalez Z (2003) Artificial immune system [AIS] research in the last five years. Evol Comput CEC’03. The 2003 Congress, 1:123–130
Saravanan K, Sasithra S (2014) Review on classification based on artificial neural networks. Int J Ambient Syst Appl 2(4):11–18
Stepney S; Robert SE, Timmis J, Tyrrell AM (2004) Towards a conceptual framework for artificial immune systems. Int Conf Artif Immune Syst LNCS 3239:52–64
Castro de LN, von Zuben F (2000b) An evolutionary immune network for data clustering. In: SBRN. Brazil, pp 187–204
Forrest AS, Allen PL, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings 1994 IEEE computer society symposium research in security and privacy, pp 202–292
Burnet F (1959) The clonal selection theory of acquired immunity. Cambridge University Press
Blake CL, Merz CJ UCI repository of machine learning databases. Dept. of Information and Computer Sciences, University of California, Irvine
Castro de LN, von Zuben F (2000a) The clonal selection algorithm with engineering. In: Proceedings of genetic and evolutionary computation. Las Vegas, USA, pp 36–37
Castro de LN, Timmis J (2002a) An artificial immune network for multimodal optimisation. In: Congress of evolutionary computation. part of the world congress on computational intelligence. Honolulu, HI, pp 699–704
Data Analysis’. Biosystems 55(1/3), pp 143–150
Hofmweyr Forest S, Somayaji SA (1998) Computer immunology. In: Proceedings of the twelfth systems administration conference [LISA’98], pp 283–290
Jackson Jacob T, Gunsch GH, Claypoole RL, Lamont GB (2003) Novel steganography detection using an artificial immune system approach. CEC IEEE, pp 139–145
Jerne N (1974) ‘Towards a network theory of the immune system’. Annals of immunology (Inst Pasteur) 125C:373–389
Jitha RT, Sivadasan ET (2015) A survey paper on various reversible data hiding techniques in encrypted images. In: International advance computing conference [IACC], 2015 IEEE international, pp 1039–1043
Prasasd Babu MS, Somesh K (2015) Artificial immune recognition systems in medical diagnosis. IEEE, pp 882–887
Tarakanov AO (2012) Information security with formal immune networks. In: Int J Comput Sci Inf Tech 3(05):54133–5136, ISSN-0975-9646
Timmis J, Neal M (2001) A resource limited artificial immune system. Knowl Syst 14(3/4):121–130
Timmis J, Neal M, Hunt J (2000) An artificial immune system for data analysis. BIOSYSTEMS 55(1/3):143–150
Duch W (2002a) Datasets used for classification: comparison of results. http://www.phys.uni.torun.pl/kmk/projects/datasets.html. Computational Intelligence Laboratory, Department of Informatics, Nicholaus Copernicus University, Torun, Poland
Duch W (2002b) Logical rules extracted from data. Computational Intelligence Laboratory, Department of Informatics, Nicholaus Copernicus University, Torun, Poland. http://www.phys.uni.torun.pl/kmk/projects/rules.html
Wang W, Tang Z (2009) Improved pattern recognition with complex artificial immune system. Soft Comput 13:1209–1217
Bahekar KB, Gupta AK (2018) Artificial immune recognition system-based classification technique. in: Proceedings of international conference on recent advancement on computer and communication. Lecture Notes in Networks and Systems 34:626–635. Springer, Singapore. https://doi.org/10.1007/978-981-10-8198-9_65
Shekhar S, Xiong H, Zhou X (2017) Artificial neural network. Springer, Encyclopaedia of GIS
Hemlata (2018) Comprehensive analysis of data mining classifiers using weka. Int J Adv Res Compute Sci 9(2):718–723, March–April 2018
Reddy S, Sai Prasad K, Mounika A (2017) Classification algorithms on data mining: a study. Int J Comput Intell Res 13(8):2135–2142
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bahekar, K.B. (2021). Comprehensive Analysis of Classification Techniques Based on Artificial Immune System and Artificial Neural Network Algorithms. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_68
Download citation
DOI: https://doi.org/10.1007/978-981-33-4604-8_68
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4603-1
Online ISBN: 978-981-33-4604-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)