Research Article

Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results

Volume: 11 Number: 1 June 30, 2025
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Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results

Abstract

This study examines the effectiveness of deep learning-based models in the classification and monitoring of fish species. A dataset obtained from the Kaggle platform, containing 31 different fish species, was used to train MobileNetV2, DenseNet121, and VGG19 models. Bayesian optimization was applied to enhance model performance and determine the optimal hyperparameters. The results indicate that models trained with Bayesian optimization achieved significantly higher accuracy compared to those trained with randomly assigned hyperparameters. Additionally, the ensemble learning approach, which combined the outputs of individual models, yielded the best classification performance. This study demonstrates that deep learning techniques serve as a crucial tool for marine ecosystem conservation and sustainable fisheries practices.

Keywords

References

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Details

Primary Language

English

Subjects

Image Processing, Pattern Recognition

Journal Section

Research Article

Early Pub Date

June 27, 2025

Publication Date

June 30, 2025

Submission Date

February 11, 2025

Acceptance Date

May 10, 2025

Published in Issue

Year 2025 Volume: 11 Number: 1

APA
Aydilek, H., & Erten, M. Y. (2025). Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results. International Journal of Pure and Applied Sciences, 11(1), 92-107. https://doi.org/10.29132/ijpas.1637721
AMA
1.Aydilek H, Erten MY. Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results. International Journal of Pure and Applied Sciences. 2025;11(1):92-107. doi:10.29132/ijpas.1637721
Chicago
Aydilek, Hüseyin, and Mustafa Yasin Erten. 2025. “Fish Species Classification With Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results”. International Journal of Pure and Applied Sciences 11 (1): 92-107. https://doi.org/10.29132/ijpas.1637721.
EndNote
Aydilek H, Erten MY (June 1, 2025) Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results. International Journal of Pure and Applied Sciences 11 1 92–107.
IEEE
[1]H. Aydilek and M. Y. Erten, “Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results”, International Journal of Pure and Applied Sciences, vol. 11, no. 1, pp. 92–107, June 2025, doi: 10.29132/ijpas.1637721.
ISNAD
Aydilek, Hüseyin - Erten, Mustafa Yasin. “Fish Species Classification With Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results”. International Journal of Pure and Applied Sciences 11/1 (June 1, 2025): 92-107. https://doi.org/10.29132/ijpas.1637721.
JAMA
1.Aydilek H, Erten MY. Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results. International Journal of Pure and Applied Sciences. 2025;11:92–107.
MLA
Aydilek, Hüseyin, and Mustafa Yasin Erten. “Fish Species Classification With Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results”. International Journal of Pure and Applied Sciences, vol. 11, no. 1, June 2025, pp. 92-107, doi:10.29132/ijpas.1637721.
Vancouver
1.Hüseyin Aydilek, Mustafa Yasin Erten. Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results. International Journal of Pure and Applied Sciences. 2025 Jun. 1;11(1):92-107. doi:10.29132/ijpas.1637721

Cited By

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