Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 529)

Abstract

One of the direct consequences of climate change lies in the spread of invasive species, which constitute a serious and rapidly worsening threat to ecology, preservation of natural biodiversity and protection of flora and fauna. It can even be a potential threat to the health of humans. These species, do not appear to have serious morphological variations, despite their strong biological differences. Due to this fact their identification process is often quite difficult. The need to protect the environment and safeguard public health, requires the development of advanced methods for early and accurate identification of some particularly dangerous invasive species, in order to plan and apply specific and effective management measures. The aim of this study is to create an advanced computer vision system for the automatic recognition of invasive or other unknown species, based on their phenotypes. More specifically, this research proposes an innovative and very effective Extreme Learning Machine (ELM) model, which is optimized by the Adaptive Elitist Differential Evolution algorithm (AEDE). The AEDE is an improved version of the differential evolution (DE) algorithm and it is proper for big data resolution. Feature selection is done by using deep learning Convolutional Neural Networks. A Geo Location system is used to detect the invasive species by Comparing with the local species of the region under research.

Keywords

Big data Invasive species Adaptive Elitist Differential Evolution Extreme Learning Machine Machine vision Convolutional Neural Networks 

References

  1. 1.
    Rahel, F., Olden, J.D.: Assessing the effects of climate change on aquatic invasive species. Soc. Conserv. Biol. 22(3), 521–533 (2008)CrossRefGoogle Scholar
  2. 2.
    Miller, W.: The structure of species, outcomes of speciation and the species problem: Ideas for paleobiology. Palaeoclimatol. Palaeoecol. 176, 1–10 (2001)CrossRefGoogle Scholar
  3. 3.
    Demertzis, K., Iliadis, L.: Intelligent bio-inspired detection of food borne pathogen by DNA barcodes: the case of invasive fish species Lagocephalus Sceleratus. Eng. Appl. Neural Netw. 517, 89–99 (2015). doi:10.1007/978-3-319-23983-5_9 CrossRefGoogle Scholar
  4. 4.
    Hornberg, A.: Handbook of Machine Vision, p. 709. Wiley, Hoboken (2006). ISBN: 978-3-527-40584-8Google Scholar
  5. 5.
    Graves, M., Batchelor, B.G.: Machine Vision for the Inspection of Natural Products, p. 5. Springer, London (2003). ISBN: 978-1-85233-525-0Google Scholar
  6. 6.
    Carsten, S., Ulrich, M., Wiedemann, C.: Machine Vision Algorithms and Applications, p. 1. Wiley-VCH, Weinheim (2008). ISBN: 978-3-527-40734-7Google Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)Google Scholar
  8. 8.
    Svellingen, C., Totland, B., White, D., Οvredal, T.: Automatic Species Recognition, length measurement and weight determination using the CatchMeter Computer Vision System (2006)Google Scholar
  9. 9.
    Cabreira, A.G., Tripode, M., Madirolas, A.: Artificial neural networks for fish-species identification. ICES J. Mar. Sci. 66, 1119–1129 (2009)CrossRefGoogle Scholar
  10. 10.
    Rova, A., Mori, G., Dill, L.M.: One fish, two fish, butterfish, trumpeter: recognizing fish in underwater video. In: Conference on Machine Vision Applications, pp. 404–407 (2007)Google Scholar
  11. 11.
    Lee, D.J., Schoenberger, R., Shiozawa, D., Xu, X., Zhan, P.: Contour matching for a fish recognition and migration monitoring system. Stud. Comput. Intell. 122, 183–207 (2008)Google Scholar
  12. 12.
    Ogunlana, S.O., Olabode, O., Oluwadare, S.A.A., Iwasokun, G.B.: Fish classification using SVM. IEEE Afr. J. Comput. ICT 8(2), 75–82 (2015)Google Scholar
  13. 13.
    Mutasem, K.A., Khairuddin, B.O., Shahrulazman, N., Ibrahim, A.: Fish recognition based on the combination between robust features selection, image segmentation and geometrical parameters techniques using artificial neural network and decision tree. J. Comput. Sci. Inf. Secur. 6(2), 215–221 (2009)Google Scholar
  14. 14.
    Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B.: Evolutionary extreme learning machine. Pattern Recogn. 38, 1759–1763 (2005)CrossRefMATHGoogle Scholar
  15. 15.
    Qu, Y., Shen, Q., Parthaláin, N.M., Wu, W.: Extreme learning machine for mammograhic risk analysis. In: UK Workshop on Computational Intelligence, pp. 1–5 (2010)Google Scholar
  16. 16.
    Sridevi, N., Subashini, P.: Combining Zernike moments with regional features for Classification of Handwritten Ancient Tamil Scripts using Extreme Learning Machine. In: IEEE IC Emerging Trends in Computing, Communication and Nanotechnology, pp. 158–162 (2013)Google Scholar
  17. 17.
    Wang, D.D., Wang, R., Yan, H.: Fast prediction of protein-protein interaction sites based on extreme learning machines. Neurocomputing 77, 258–266 (2014)CrossRefGoogle Scholar
  18. 18.
    Bazi, Y., Alajlan, N., Melgani, F., AlHichri, H., Malek, S., Yager, R.R.: Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images. IEEE Geosci. Remote Sens. Lett. 11, 1066–1070 (2014)CrossRefGoogle Scholar
  19. 19.
    Zhao, X.: A perturbed particle swarm algorithm for numerical optimization. Appl. Soft Comput. 10(1), 119–124 (2010). doi:10.1016/j.asoc.2009.06.010 CrossRefGoogle Scholar
  20. 20.
    Li, X., Yin, M.: Application of differential evolution algorithm on self-potential data. PLoS ONE 7(12), e51199 (2012). doi:10.1371/journal.pone.0051199 CrossRefGoogle Scholar
  21. 21.
    Gandomi, A.H., Yang, X.-S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wang, G.-G., Guo, L., Duan, H., Wang, H.: A new improved firefly algorithm for global numerical optimization. J. Comput. Theor. Nanosci. 11(2), 477–485 (2014). doi:10.1166/jctn.2014.3383 CrossRefGoogle Scholar
  23. 23.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). doi:10.1016/j.advengsoft.2013.12.007 CrossRefGoogle Scholar
  24. 24.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001). doi:10.1177/003754970107600201 CrossRefGoogle Scholar
  25. 25.
    Simon, D.: Biogeography-based optimization. IEEE Trans. Evolut. Comput. 12(6), 702–713 (2008). doi:10.1109/TEVC.2008.919004 CrossRefGoogle Scholar
  26. 26.
    Li, X., Zhang, J., Yin, M.: Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput. Appl. 24(7–8), 1867–1877 (2014). doi:10.1007/s00521-013-1433-8 CrossRefGoogle Scholar
  27. 27.
    Mirjalili, S., Mohd Hashim, S.Z., Moradian Sardroudi, H.: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 218(22), 11125–11137 (2012). doi:10.1016/j.amc.2012.04.069 MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Zhang, Z., Zhang, N., Feng, Z.: Multi-satellite control resource scheduling based on ant colony optimization. Expert Syst. Appl. 41(6), 2816–2823 (2014). doi:10.1016/j.eswa.2013.10.014 CrossRefGoogle Scholar
  29. 29.
    Gandomi, A.H.: Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans. 53(4), 1168–1183 (2014). doi:10.1016/j.isatra.2014.03.018 CrossRefGoogle Scholar
  30. 30.
    Ho-Huu, V., Nguyen-Thoi, T., Vo-Duy, T., Nguyen-Trang, T.: An adaptive elitist differential evolution for optimization of truss structures with discrete design variables. Comput. Struct. 165, 59–75 (2016)CrossRefGoogle Scholar
  31. 31.
    Bluche, T., Ney, H., Kermorvant, C.: Feature extraction with convolutional neural networks for handwritten word recognition. In: 12th International Conference on Document Analysis and Recognition, pp. 285–289. IEEE (2013)Google Scholar
  32. 32.
    Anantharajah, K., Ge, Z., McCool, C., Denman, S., Fookes, C., Corke, P., Tjondronegoro, D., Sridharan, S.: Local inter-session variability modelling for object classification. In: IEEE Winter Conference on Applications of Computer Vision (WACV 2014). Steamboat Springs, Co., 24–26 March 2014Google Scholar
  33. 33.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. IJCV (2015). arXiv:1409.0575
  34. 34.
    Jia, D.J., Vinyals, Y., Hoffman, O., Zhang, J., Tzeng, N., Darrell, E.T.: Decaf: a deep convolutional activation feature for generic visual recognition. CoRR, abs/1310.1531 (2013)Google Scholar
  35. 35.
    Cambria, E., Huang, G.-B.: Extreme learning machines. IEEE Intell. Syst. 28, 37–134 (2013)CrossRefGoogle Scholar
  36. 36.
    Price, K., Storn, M., Lampinen, A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005). ISBN: 978-3-540-20950-8MATHGoogle Scholar
  37. 37.
  38. 38.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Lab of Forest-Environmental Informatics and Computational IntelligenceDemocritus University of ThraceN OrestiadaGreece

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