Skip to main content
SpringerLink
Log in

Immunocomputing for intelligent signal processing

  • AIS
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Based on immunocomputing (IC), this paper describes an approach to intelligent signal processing. The approach includes both low-level feature extraction and high-level (intelligent) pattern recognition. The key model is the formal immune network (FIN), which includes apoptosis (programmed cell death) and immunization and controls them by cytokines (messenger proteins). Such FIN can be formed from the signal or combination of signals using discrete tree transform, singular value decomposition and the proposed index of inseparability as a measure of quality of FIN. Several comparisons with neural computing and support vector machines suggest that IC may outperform state of the art approaches to intelligent signal processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Dasgupta D (ed) (1999) Artificial immune systems and their applications. Springer, Berlin

    MATH  Google Scholar 

  2. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, London

    MATH  Google Scholar 

  3. Tarakanov AO, Skormin VA, Sokolova SP (2003) Immunocomputing: principles and applications. Springer, New York

    MATH  Google Scholar 

  4. Zhao W (2005) Review of “Immunocomputing: principles and applications”. ACM SIGACT News 36(4):14–17

    Article  Google Scholar 

  5. Tarakanov A, Nicosia G (2007) Foundations of immunocomputing. In: Proceeding of the 1st IEEE symposium on foundations of computational intelligence (FOCI’07), Honolulu, Hawaii, pp 503–508

  6. Tarakanov AO, Tarakanov YA (2005) A comparison of immune and genetic algorithms for two real-life tasks of pattern recognition. Int J Unconv Comput 1(4):357–374

    MathSciNet  Google Scholar 

  7. Tarakanov AO, Tarakanov YA (2004) A comparison of immune and neural computing for two real-life tasks of pattern recognition. LNCS 3239:236–249

    Google Scholar 

  8. Chao DL, Davenport MP, Forrest S, Perelson AS (2004) A stochastic model of cytotoxic T cell responses. J Theor Biol 228:227–240

    Article  MathSciNet  Google Scholar 

  9. Dasgupta D, Krishna-Kumar K, Wong D, Berry M (2004) Negative selection algorithm for aircraft fault detection. LNCS 3239:1–13

    Google Scholar 

  10. Dasgupta D, Gonzalez F (2005) Artificial immune systems in intrusion detection. In: Rao Vemuri V (ed) Enhancing computer security with smart technology. Auerbach Publications, Boca Raton, FL, pp 165–208

    Google Scholar 

  11. Ji Z, Dasgupta D (2007) Revisiting negative selection algorithms. Evol Comput J 15(2):223–251

    Article  Google Scholar 

  12. Balachandran S, Dasgupta D, Nino F, Garrett D (2007) A framework for evolving multi-shaped detectors in negative selection. In: Proceeding of the 1st IEEE symposium on foundations of computational intelligence (FOCI’07), Honolulu, Hawaii, pp 401–408

  13. Dasgupta D (2006) Advances in artificial immune systems. IEEE Comput Intell Mag 1(4):40–49

    Google Scholar 

  14. Dasgupta D, Nino F (2008) Immunological computation: theory and applications. Auerbach Publications, Boca Raton, FL

    Book  Google Scholar 

  15. Goncharova LB, Jacques Y, Martin-Vide C, Tarakanov AO, Timmis JI (2005) Biomolecular immune-computer: theoretical basis and experimental simulator. LNCS 3627:72–85

    Google Scholar 

  16. Tarakanov AO, Goncharova LB, Tarakanov YA (2010) Carbon nanotubes in medicinal biochips. WIREs Nanomed Nanobiotechnol 2(1):1–10

    Article  Google Scholar 

  17. Goncharova LB, Tarakanov AO (2007) Molecular networks of brain and immunity. Brain Res Rev 55(1):155–166

    Article  Google Scholar 

  18. Agnati LF, Tarakanov AO, Guidolin D (2005) A simple mathematical model of cooperativity in receptor mosaics based on the “symmetry rule”. BioSystems 80(2):165–173

    Article  Google Scholar 

  19. Agnati LF, Tarakanov AO, Ferre S, Fuxe K, Guidolin D (2005) Receptor-receptor interactions, receptor mosaics, and basic principles of molecular network organization: possible implications for drug development. J Mol Neurosci 26(2–3):193–208

    Article  Google Scholar 

  20. Agnati LF, Fuxe KG, Goncharova LB, Tarakanov AO (2008) Receptor mosaics of neural and immune communication: possible implications for basal ganglia functions. Brain Res Rev 58(2):400–414

    Article  Google Scholar 

  21. Goncharova LB, Tarakanov AO (2008) Why chemokines are cytokines while their receptors are not cytokine ones? Curr Med Chem 15(13):1297–1304

    Article  Google Scholar 

  22. Goncharova LB, Tarakanov AO (2008) Nanotubes at neural and immune synapses. Curr Med Chem 15(3):210–218

    Article  Google Scholar 

  23. Adamatzky A (1994) Identification of cellular automata. Taylor & Francis, London

    MATH  Google Scholar 

  24. Tarakanov A, Adamatzky A (2002) Virtual clothing in hybrid cellular automata. Kybernetes 31(7–8):394–405

    Google Scholar 

  25. Tarakanov A, Prokaev A (2007) Identification of cellular automata by immunocomputing. J Cell Autom 2(1):39–45

    MATH  MathSciNet  Google Scholar 

  26. Tarakanov A (2007) Formal immune networks: self-organization and real-world applications. In: Prokopenko M (ed) Advances in applied self-organizing systems. Springer, London, pp 271–290

    Google Scholar 

  27. Tarakanov A, Prokaev A, Varnavskikh E (2007) Immunocomputing of hydroacoustic fields. Int J Unconv Comput 3(2):123–133

    Google Scholar 

  28. Tarakanov AO, Sokolova LA, Kvachev SV (2007) Intelligent simulation of hydrophysical fields by immunocomputing. LNGC XIV:252–262

    Google Scholar 

  29. Atreas ND, Karanikas CG, Tarakanov AO (2003) Signal processing by an immune type tree transform. LNCS 2787:111–119

    Google Scholar 

  30. Karanikas C, Prios G (2003) A non-linear discrete transform for pattern recognition of discrete chaotic systems. Chaos Solitons Fractals 17:195–201

    Article  MathSciNet  Google Scholar 

  31. Atreas ND, Karanikas C, Polychronidou P (2004) Signal analysis on strings for immune-type pattern recognition. Compar Funct Genomics 5:69–74

    Article  Google Scholar 

  32. Tarakanov AO, Kvachev SV, Sukhorukov AV (2005) A formal immune network and its implementation for on-line intrusion detection. LNCS 3685:394–405

    Google Scholar 

  33. Tarakanov A, Kryukov I, Varnavskikh E, Ivanov V (2007) A mathematical model of intrusion detection by immunocomputing for spatially distributed security systems. RadioSystems 106:90–92 (in Russian)

    Google Scholar 

  34. Tarakanov AO (2007) Mathematical models of intrusion detection by an intelligent immunochip. CCIS (LNCS) 1:308–319

    MATH  Google Scholar 

  35. Horn R, Johnson C (1986) Matrix analysis. Cambridge University Press, Cambridge

    Google Scholar 

  36. Tarakanov AO, Goncharova LB, Tarakanov OA (2005) A cytokine formal immune network. LNAI (LNCS) 3630:510–519

    Google Scholar 

  37. Renyi A (1961) On measures of entropy and information. In: Proceedings of the 4th Berkeley symposium on mathematics, statistics and probability, Vol 1. pp 547–561

  38. Johnson JE (2005) Networks, Markov Lie monoids, and generalized entropy. LNCS 3685:129–135

    Google Scholar 

  39. Bay SD (1999) The UCI KDD Archive: Irvine, CA, University of California, Department of Information and Computer Science, http://kdd.ics.uci.edu

  40. Joachims T (1999) Making large-scale SVM learning practical. In: Scholkopf B, Burges C, Smola A (eds) Advances in kernel methods—support vector learning. MIT-Press, Cambridge, MA

    Google Scholar 

  41. Joachims T (2003) Learning to classify text using support vector machines: methods, theory, and algorithms. Kluwer, New York

    Google Scholar 

  42. Joachims T (2004) SVM-light: support vector machine, http://svmlight.joachims.org

  43. Ivanciuc Q (2007) Applications of support vector machines in chemistry. Rev Comput Chem 23:291–400

    Article  Google Scholar 

  44. Yao JT, Zhao SL, Fan L (2006) An enhanced support vector machine model for intrusion detection. LNAI 4062:538–543

    Google Scholar 

  45. Tarakanov AO (2008) Immunocomputing for intelligent intrusion detection. IEEE Comput Intell Mag 3(2):22–30

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, 14-Line 39, St. Petersburg, 199178, Russia

    Alexander O. Tarakanov

Authors
  1. Alexander O. Tarakanov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander O. Tarakanov.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tarakanov, A.O. Immunocomputing for intelligent signal processing. Neural Comput & Applic 19, 1143–1152 (2010). https://doi.org/10.1007/s00521-010-0391-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0391-7

Keywords

Search

Navigation