An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks
Bangladesh has plentiful water, which is essential to its freshwater fish traditions. Environmental concerns and other causes have reduced the country's water resources, threatening many native freshwater fish species. Thus, the younger generation deficiencies recognition of local freshwater fi...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Automatika |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2457803 |
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| author | Asadullah Shaikh Wahidur Rahman Kaniz Roksana Tarequl Islam Mohammad Motiur Rahman Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan |
| author_facet | Asadullah Shaikh Wahidur Rahman Kaniz Roksana Tarequl Islam Mohammad Motiur Rahman Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan |
| author_sort | Asadullah Shaikh |
| collection | DOAJ |
| description | Bangladesh has plentiful water, which is essential to its freshwater fish traditions. Environmental concerns and other causes have reduced the country's water resources, threatening many native freshwater fish species. Thus, the younger generation deficiencies recognition of local freshwater fish and struggles to recognize them. Traditional methods are very insufficient to overcome these issues. To address these research gaps, the research proposes an automatic system for categorizing Bangladesh's freshwater fish. The proposed methodology involves several key steps, including building a comprehensive dataset, extracting features from fish images using pre-trained Convolutional Neural Network (CNN) models, and employing typical ML approaches. Initially comprising eight classes, the dataset undergoes feature extraction using CNN algorithms, followed by the utilization of various feature selection methods such as Support Vector Classifier, Principal Component Analysis, Linear Discriminant Analysis, and optimization models like Particle Swarm Optimization, Bacterial Foraging Optimization, and Cat Swarm Optimization. In the final phase, seven conventional ML techniques are applied to classify the images of local fishes. Empirical measurements are gathered and analyzed to assess the proposed framework's performance. Particularly, the present approach achieves the highest accuracy of 98.71% through the utilization of the ML mechanism Logistic Regression with Resnet50, SVC, and CSO models. |
| format | Article |
| id | doaj-art-00d0b1d5f5d94a84809b95980cda8cc6 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-00d0b1d5f5d94a84809b95980cda8cc62025-08-20T03:48:13ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-04-0166224928010.1080/00051144.2025.2457803An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networksAsadullah Shaikh0Wahidur Rahman1Kaniz Roksana2Tarequl Islam3Mohammad Motiur Rahman4Hani Alshahrani5Adel Sulaiman6Mana Saleh Al Reshan7Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi ArabiaDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, BangladeshDepartment of Computer Science and Engineering, Uttara University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Khwaja Yunus Ali University, Sirajganj, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, BangladeshEmerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, Saudi ArabiaEmerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi ArabiaBangladesh has plentiful water, which is essential to its freshwater fish traditions. Environmental concerns and other causes have reduced the country's water resources, threatening many native freshwater fish species. Thus, the younger generation deficiencies recognition of local freshwater fish and struggles to recognize them. Traditional methods are very insufficient to overcome these issues. To address these research gaps, the research proposes an automatic system for categorizing Bangladesh's freshwater fish. The proposed methodology involves several key steps, including building a comprehensive dataset, extracting features from fish images using pre-trained Convolutional Neural Network (CNN) models, and employing typical ML approaches. Initially comprising eight classes, the dataset undergoes feature extraction using CNN algorithms, followed by the utilization of various feature selection methods such as Support Vector Classifier, Principal Component Analysis, Linear Discriminant Analysis, and optimization models like Particle Swarm Optimization, Bacterial Foraging Optimization, and Cat Swarm Optimization. In the final phase, seven conventional ML techniques are applied to classify the images of local fishes. Empirical measurements are gathered and analyzed to assess the proposed framework's performance. Particularly, the present approach achieves the highest accuracy of 98.71% through the utilization of the ML mechanism Logistic Regression with Resnet50, SVC, and CSO models.https://www.tandfonline.com/doi/10.1080/00051144.2025.2457803 |
| spellingShingle | Asadullah Shaikh Wahidur Rahman Kaniz Roksana Tarequl Islam Mohammad Motiur Rahman Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks Automatika |
| title | An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks |
| title_full | An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks |
| title_fullStr | An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks |
| title_full_unstemmed | An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks |
| title_short | An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks |
| title_sort | improved deep cnn based freshwater fish classification with cascaded bio inspired networks |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2025.2457803 |
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