nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

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

verfasst von : Konstantinos Demertzis, Lazaros Iliadis

Erschienen in: Advances in Big Data

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Unified Retrieval Model of Big Data

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

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

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 CrossRef

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

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

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

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)

Svellingen, C., Totland, B., White, D., Οvredal, T.: Automatic Species Recognition, length measurement and weight determination using the CatchMeter Computer Vision System (2006)

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

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)

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)

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)

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)

14.

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

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)

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)

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)CrossRef

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)CrossRef

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 CrossRef

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 CrossRef

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)MathSciNetCrossRef

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 CrossRef

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 CrossRef

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 CrossRef

25.

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

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 CrossRef

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 MathSciNetCrossRefMATH

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 CrossRef

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 CrossRef

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)CrossRef

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)

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

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)

35.

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

36.

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

37.

https://github.com/che0/countries

38.

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

Titel: Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species
verfasst von: Konstantinos Demertzis
Lazaros Iliadis
Verlag: Springer International Publishing
Buch: Advances in Big Data
Print ISBN: 978-3-319-47897-5

Electronic ISBN: 978-3-319-47898-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-47898-2_34

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"