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

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Advances in Big Data (INNS 2016)

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

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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.

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References

  1. Rahel, F., Olden, J.D.: Assessing the effects of climate change on aquatic invasive species. Soc. Conserv. Biol. 22(3), 521–533 (2008)

    Article  Google Scholar 

  2. Miller, W.: The structure of species, outcomes of speciation and the species problem: Ideas for paleobiology. Palaeoclimatol. Palaeoecol. 176, 1–10 (2001)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  4. Hornberg, A.: Handbook of Machine Vision, p. 709. Wiley, Hoboken (2006). ISBN: 978-3-527-40584-8

    Google Scholar 

  5. Graves, M., Batchelor, B.G.: Machine Vision for the Inspection of Natural Products, p. 5. Springer, London (2003). ISBN: 978-1-85233-525-0

    Google Scholar 

  6. Carsten, S., Ulrich, M., Wiedemann, C.: Machine Vision Algorithms and Applications, p. 1. Wiley-VCH, Weinheim (2008). ISBN: 978-3-527-40734-7

    Google Scholar 

  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. 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. Cabreira, A.G., Tripode, M., Madirolas, A.: Artificial neural networks for fish-species identification. ICES J. Mar. Sci. 66, 1119–1129 (2009)

    Article  Google Scholar 

  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. 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. 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. 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. Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B.: Evolutionary extreme learning machine. Pattern Recogn. 38, 1759–1763 (2005)

    Article  MATH  Google Scholar 

  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. 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. Wang, D.D., Wang, R., Yan, H.: Fast prediction of protein-protein interaction sites based on extreme learning machines. Neurocomputing 77, 258–266 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  25. Simon, D.: Biogeography-based optimization. IEEE Trans. Evolut. Comput. 12(6), 702–713 (2008). doi:10.1109/TEVC.2008.919004

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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 2014

    Google Scholar 

  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. 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. Cambria, E., Huang, G.-B.: Extreme learning machines. IEEE Intell. Syst. 28, 37–134 (2013)

    Article  Google Scholar 

  36. Price, K., Storn, M., Lampinen, A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005). ISBN: 978-3-540-20950-8

    MATH  Google Scholar 

  37. https://github.com/che0/countries

  38. http://www.cabi.org/isc/

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Author information

Authors and Affiliations

  1. Lab of Forest-Environmental Informatics and Computational Intelligence, Democritus University of Thrace, 193 Pandazidoust., 68200, N Orestiada, Greece

    Konstantinos Demertzis & Lazaros Iliadis

Authors
  1. Konstantinos Demertzis
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  2. Lazaros Iliadis
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Corresponding author

Correspondence to Konstantinos Demertzis .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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Demertzis, K., Iliadis, L. (2017). Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_34

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

  • eBook Packages: EngineeringEngineering (R0)

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