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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.
Abstract: Classification of fish species in aquatic pictures is a growing field of research for researchers and image processing experts. Classification of fish species in aquatic images is critical for fish analytical purposes, such as ecological auditing balance, observing fish populations, and saving threatened animals. However, ocean water scattering and absorption of light result in dim and low contrast pictures, making fish classification laborious and challenging. This paper presents an efficient scheme of fish classification, which helps the biologist understand varieties of fish and their surroundings. This proposed system used an improved deep learning-based auto encoder decoder method for fish classification. Optimal feature selection is a major issue with deep learning models generally. To solve this problem efficiently, an enhanced grey wolf optimization technique (EGWO) has been introduced in this study. The accuracy of the classification system for aquatic fish species depends on the essential texture features. Accordingly, in this study, the proposed EGWO has selected the most optimal texture features from the features extracted by the auto encoder. Finally, to prove the efficacy of the proposed method, it is compared to existing deep learning models such as AlexNet, Res Net, VGG Net, and CNN. The proposed method is analysed by varying iterations, batches, and fully connected layers. The analysis of performance criteria such as accuracy, sensitivity, specificity, precision, and F1 score reveals that AED-EGWO gives superior performance.
J. M. Jini Mol and S. Albin Jose, “Fish Species Classification using Optimized Deep Learning Model” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130996
@article{Mol2022,
title = {Fish Species Classification using Optimized Deep Learning Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130996},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130996},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {J. M. Jini Mol and S. Albin Jose}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.