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- Title
Classification of Ecological Data by Deep Learning.
- Authors
Liu, Shaobo; Shih, Frank Y.; Russell, Gareth; Russell, Kimberly; Phan, Hai
- Abstract
Ecologists have been studying different computational models in the classification of ecological species. In this paper, we intend to take advantages of variant deep-learning models, including LeNet, AlexNet, VGG models, residual neural network, and inception models, to classify ecological datasets, such as bee wing and butterfly. Since the datasets contain relatively small data samples and unbalanced samples in each class, we apply data augmentation and transfer learning techniques. Furthermore, newly designed inception residual and inception modules are developed to enhance feature extraction and increase classification rates. As comparing against currently available deep-learning models, experimental results show that the proposed inception residual block can avoid the vanishing gradient problem and achieve a high accuracy rate of 92%.
- Subjects
DEEP learning; CONVOLUTIONAL neural networks; FEATURE extraction; CLASSIFICATION
- Publication
International Journal of Pattern Recognition & Artificial Intelligence, 2020, Vol 34, Issue 13, pN.PAG
- ISSN
0218-0014
- Publication type
Article
- DOI
10.1142/S0218001420520102