Classification of Ecological Data by Deep Learning

Document Type

Article

Publication Date

12-15-2020

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%.

Identifier

85084395251 (Scopus)

Publication Title

International Journal of Pattern Recognition and Artificial Intelligence

External Full Text Location

https://doi.org/10.1142/S0218001420520102

ISSN

02180014

Issue

13

Volume

34

Fund Ref

National Science Foundation

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