Immunocomputing for Geoinformation Fusion and Forecast

  • Alexander TarakanovEmail author
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Based on immunocomputing (IC), this paper proposes a new way for geoinformation fusion, spatio-temporal modeling, and forecast. The approach includes mathematically, rigorous mapping of high-dimensional spatio-temporal data into a scalar index, discrete tree transform (DTT) of the index values into states of cellular automata (CA), and identification of CA by IC. Numerical examples use official data of International Association for the Development of Freediving (AIDA), World Health Organization (WHO), as well as time series of Solar Influences Data Analysis Center (SIDC) and National Aeronautics and Space Administration (NASA). Anomaly index is also proposed using special the case of DTT. Recent results suggest that the IC approach outperforms (by training time and accuracy) state-of-the-art approaches of computational intelligence.


Immunocomputing Geoinformation fusion Spatiotemporal modeling Forecast 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kuznetsov VI, Gubanov AF, Kuznetsov VV, Tarakanov AO, Tchertov OG (1999) Map of complex appraisal of environmental conditions in Kaliningrad (in Russian and English). In: Kaliningrad. Ecological atlasGoogle Scholar
  2. 2.
    AIDA: International Association for the Development of Freediving (Apnoe),
  3. 3.
    WHO: World Health Organization,
  4. 4.
    Tarakanov AO, Skormin VA, Sokolova SP (2003) Immunocomputing: Principles and Applications. Springer, New YorkGoogle Scholar
  5. 5.
    Goncharova LB, Tarakanov AO (2007) Molecular networks of brain and immunity. Brain Research Reviews 55/1, pp 155-166CrossRefGoogle Scholar
  6. 6.
    Goncharova LB, Tarakanov AO (2008) Nanotubes at neural and immune synapses. Current Medicinal Chemistry 15/3, pp 210-218Google Scholar
  7. 7.
    Goncharova LB, Tarakanov AO (2008) Why chemokines are cytokines while their receptors are not cytokine ones? Current Medicinal Chemistry 15(13), pp 1297-1304CrossRefGoogle Scholar
  8. 8.
    Agnati LF, Fuxe KG, Goncharova LB, Tarakanov AO (2008) Receptor mosaics of neural and immune communication: possible implications for basal ganglia functions. Brain Research Reviews 58(2), pp 400-414CrossRefGoogle Scholar
  9. 9.
    Fuxe KG, Tarakanov AO, Goncharova LB, Agnati LF (2008) A new road to neuroinflammation in Parkinson's disease? Brain Research Reviews 58/2, pp 453-458CrossRefGoogle Scholar
  10. 10.
    Tarakanov AO (2008) Immunocomputing for intelligent intrusion detection. J IEEE Computational Intelligence Magazine 3/2 (special issue Cyber Security), pp 22-30CrossRefGoogle Scholar
  11. 11.
    Tarakanov A, Prokaev A, Varnavskikh E (2007) Immunocomputing of hydroacoustic fields. J International Journal of Unconventional Computing 3/2, pp 123-133Google Scholar
  12. 12.
    Tarakanov AO, Sokolova LA, Kvachev SV (2007) Intelligent simulation of hydrophysical fields by immunocomputing. Lecture Notes in Geoinformation and Cartography, vol. XIV, pp 252-262. Springer, BerlinGoogle Scholar
  13. 13.
    Tarakanov AO (2008) Immunocomputing for spatio-temporal forecast. In: Mo, H. (ed) Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies. IGI Global, Hershey PA (in press)Google Scholar
  14. 14.
    Tarakanov A, Prokaev A (2007) Identification of cellular automata by immunocomputing. J Journal of Cellular Automata 2(1): 39-45Google Scholar
  15. 15.
    Atreas ND, Karanikas CG, Tarakanov AO (2003) Signal processing by an immune type tree transform. LNCS, vol. 2787, Springer, Berlin, pp 111-119Google Scholar
  16. 16.
    SIDC: Solar Influences Data Analysis Center,
  17. 17.
    NASA: Ocean Color Time-Series Project,
  18. 18.
    Cover TM, Hart PE (1967) Nearest neighbor pattern classification. J IEEE Transactions on Information Theory 13(1): 21-27CrossRefGoogle Scholar
  19. 19.
    Ivanciuc Q (2007) Applications of support vector machines in chemistry. Reviews in Computational Chemistry 23: 291-400CrossRefGoogle Scholar
  20. 20.
    Yao JT, Zhao SL, Fan Level (2006) An enhanced support vector machine model for intrusion detection. LNAI, vol 4062, pp 538-543. Springer, BerlinGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation of the RASPetersburgRussia

Personalised recommendations