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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Comparative Study on Discrimination Methods for Identifying Dangerous Red Tide Species Based on Wavelet Utilized Classification Methods

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 4, No. 1 · Published 2013 · Cited by 10

DOI: https://doi.org/10.14569/IJACSA.2013.040114

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Abstract

Comparative study on discrimination methods for identifying dangerous red tide species based on wavelet utilized classification methods is conducted. Through experiments, it is found that classification performance with the proposed wavelet derived shape information extracted from the microscopic view of the phytoplankton is effective for identifying dangerous red tide species among the other red tide species rather than the other conventional texture, color information.

Keywords

hue feature texture information wavelet descripter red tide phytoplankton idintification

How to Cite this Article

Arai, K. (2013). Comparative Study on Discrimination Methods for Identifying Dangerous Red Tide Species Based on Wavelet Utilized Classification Methods. International Journal of Advanced Computer Science and Applications, 4(1). https://doi.org/10.14569/IJACSA.2013.040114

Arai, Kohei. "Comparative Study on Discrimination Methods for Identifying Dangerous Red Tide Species Based on Wavelet Utilized Classification Methods." International Journal of Advanced Computer Science and Applications, vol. 4, no. 1, 2013, https://doi.org/10.14569/IJACSA.2013.040114.

@article{Arai2013,
  title     = {Comparative Study on Discrimination Methods for Identifying Dangerous Red Tide Species Based on Wavelet Utilized Classification Methods},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {1},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2013.040114},
  url       = {https://doi.org/10.14569/IJACSA.2013.040114}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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