Artificial neural networks for fish-species identification

ICES Journal of Marine Science

Volumn 66, Issue 6, 2009, Pages 1119-1129

  Cabreira, Ariel G ^a   Tripode, Martín ^a   Madirolas, Adriáln ^a  

^a INSTITUTO NACIONAL DE INVESTIGACIÓN Y DESARROLLO PESQUERO INIDEP   (Argentina)

Author keywords

Acoustics; Artificial neural network; Fish schools; Species identification

Indexed keywords

ENGRAULIDAE; MICROMESISTIUS POUTASSOU; SPRATTUS SPRATTUS; TRACHURUS LATHAMI;

EID: 73649099933     PISSN: 10543139     EISSN: 10959289     Source Type: Journal    
DOI: 10.1093/icesjms/fsp009     Document Type: Conference Paper

References (28)

1. Alotaibi, Y. A. 2005. Investigating spoken Arabic digits in speech recognition setting. Information Sciences, 173:115-139. (Pubitemid 40691099)
2. Balakrishnan, S., Popplewell, N., and Thomlinson, M. 2000. Intelligent robotic assembly. Computers and Industrial Engineering, 38:467-478. (Pubitemid 32029915)
3. Blanzieri, E. 2003. Theoretical interpretations and applications of radial basis function networks. Department of Information and Communication Technology, University of Trento.
4. Coetzee, J. 2000. Use of a shoal analysis and patch estimation system (SHAPES) to characterize sardine schools. Aquatic Living Resources, 13:1-10. (Pubitemid 30199536)
5. Diner, N. 2001. Correction on school geometry and density: approach based on acoustic image simulation. Aquatic Living Resources, 14:211-222. (Pubitemid 32760395)
6. Foote, K. G. 1987. Fish target strengths for use in echo-integration surveys. Journal of the Acoustical Society of America, 82:981-987.
7. Foote, K. G., Knudsen, H. P., Korneliussen, R. J., Nordbø, P. E., and Røang, K. 1991. Postprocessing system for echo sounder data. Journal of the Acoustical Society of America, 90:37-47.
8. Frize, M., Ennett, C. M., Stevenson, M., and Trigg, H. C. E. 2001. Clinical decision support systems for intensive care units: using artificial neural networks. Medical Engineering and Physics, 23:217-225. (Pubitemid 32536807)
9. Ganchev, T., Fakotakis, N., and Kokkinakis, G. 2002. Speaker verification system based on probabilistic neural networks. NIST Speaker Recognition Evaluation Workshop, 19-22 May 2002, Vienna, VA, USA.
10. Haralabous, J., and Georgakarakos, S. 1996. Artificial neural networks as a tool for species identification of fish schools. ICES Journal of Marine Science, 53:173-180.
11. Ingres. 2007. Database v. 9.1. Ingres Corp. Inc., Redwood City, CA. http://www.ingres.com.
12. Izadifar, M., and Jahromi, M. Z. 2007. Application of genetic algorithm for optimization of vegetable oil hydrogenation process. Journal of Food Engineering, 78:1-8. (Pubitemid 44062041)
13. Kohonen, T. 2001. Self-organizing Maps, 3rd edn. Springer, Berlin. 501 pp.
14. Lawson, G. L., Barange, M., and Fréon, P. 2001. Species identification of pelagic fish schools on the South African continental shelf using acoustic descriptors and ancillary information. ICES Journal of Marine Science, 58:275-287. (Pubitemid 32453921)
15. Lisboa, P. J. 2002. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks, 15:11-39. (Pubitemid 34234792)
16. Lisboa, P. J., and Taktak, A. F. G. 2006. The use of artificial neural networks in decision support in cancer: a systematic review. Neural Networks, 19:408-415. (Pubitemid 44183627)
17. Mandal, S., and Prabaharan, N. 2006. Ocean wave forecasting using recurrent neural networks. Ocean Engineering, 33:1401-1410. (Pubitemid 43280654)
18. Maneva, G. M., Procureurb, J., and Temnikova, P. P. 2003. Neural nets for ground-based g-ray astronomy. Nuclear Instruments and Methods in Physics Research A, 502:789-791.
19. Maravelias, C. D., Haralabous, J., and Papaconstantinou, C. 2003. Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Marine Ecology Progress Series, 255:249-258. (Pubitemid 36872417)
20. Paschalis, P., Giokarisa, N. D., Karabarbounisa, A., Loudosb, G. K., Maintasc, D., Papanicolasa, C. N., Spanoudakia, V., et al. 2004. Tomographic image reconstruction using artificial neural networks. Nuclear Instruments and Methods in Physics Research A, 527:211-215.
21. Ramani, N., and Patrick, P. 1992. Fish detection and classification using neural networks-some laboratory results. IEEE Journal of Oceanic Engineering, 17:364-368. (Pubitemid 23580912)
22. Reid, D., Scalabrin, C., Petitgas, P., Masse, J., Aukland, R., Carrera, P., and Georgakarakos, S. 2000. Standard protocols for the analysis of school based data from echo sounder surveys. Fisheries Research, 47:125-136. (Pubitemid 30390810)
23. Scalabrin, C., Diner, N., Weill, A., Hillion, A., and Mouchot, M. 1996. Narrowband acoustic identification of monospecific fish shoals. ICES Journal of Marine Science, 53:181-188.
24. Scalabrin, C., and Massé, J. 1993. Acoustic detection of the spatial and temporal distribution of fish shoals in the Bay of Biscay. Aquatic Living Resources, 6:269-283.
25. Simmonds, E. J., Armstrong, F., and Copland, P. J. 1996. Species identification using wideband backscatter with neural network and discriminant analysis. ICES Journal of Marine Science, 53:189-195.
26. SonarData. 2005. Echoview Version 4.10.67. SonarData Pty, Tasmania, Australia. http://www.sonardata.com/sonardata/WebHelp/Echoview.htm.
27. Zakharia, M. E., Magand, F., Hetroit, F., and Diner, N. 1996. Wideband sounder for fish species identification at sea. ICES Journal of Marine Science, 53:203-208.
28. Zar, J. H. 1984. Biostatistical Analysis. Prentice-Hall International, London. 718 pp.