WHALE AND DOLPHIN CLASSIFICATION USING ENSEMBLE TRANSFER LEARNING

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2022-12-01T06:00:00.000Z

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Abstract

Although whales and dolphins are endangered due to reasons such as global warming and improper hunting, they play an important role in the lives of other living things by providing almost 50% of the world's oxygen, which motivated this study. The datasets in this study were provided by Happywhale through Kaggle, and there are about 48 thousand images, including 31 thousand whale and 16 thousand dolphin images. Three major species from whales and dolphins, respectively, were selected from the original dataset. This study developed a novel classification model that might help marine mammal scientists monitor endangered whales and dolphins. We implemented an ensemble transfer learning model to improve classification performance, where we combined five pre-trained CNN models with specific selected datasets. The performance of classification model was measured with four metrics including accuracy, precision, recall, and F1 score. The proposed ensemble transfer learning model performs overall better than individual models for selected dataset. Although we encountered hardware requirements, limitations, and challenges in executing the ensemble transfer learning model with large size datasets, we gained experience for other pre-trained CNN models we could investigate further in future.

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Computer Science

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