Scopus Indexed Publications
Paper Details
- Title
- CNN Modeling for Recognizing Local Fish
- Author
- Ashif Raihan, Md. Ismail Jabiullah, Md. Mehedi Hasan, Md. Tarek Habib, Md. Zahed Hossain Monju,
- Abstract
- Automatic fish recognition is a challenging problem as far as machine vision is concerned. In any case, there is no mechanized gadget accessible that can recognize the fish and deal with an understanding in Bangladesh. This paper investigates fish recognition using multi-picture classification including deep learning procedures. For image processing and classification, TensorFlow Keras library is used in this work. The most famous image recognition deep learning model Convolutional Neural Network (CNN) is used to assess the dependability of our work. We have implemented three custom-built CNN models to see which one exhibits the best performance. To find the most effective model, the hyperparameter tuning technique is used. We have closely observed the matrix of parameters and performance to find the best model. After that model M2 is selected for real-life prediction as it has produced the highest accuracy of about 99.5%. The intended application will be helpful for the visually impaired, child, and ignorant to recognize the Bangladeshi fish.
- Keywords
- Deep learning , Image classification , Fish recognition , Prediction , CNN
- Journal or Conference Name
- 2021 24th International Conference on Computer and Information Technology (ICCIT)
- Publication Year
- 2021
- Indexing
- scopus