Classification of Ecological Data by Deep Learning

Shaobo Liu, Frank Y. Shih, Gareth Russell, Kimberly Russell, Hai Phan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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%.

Original languageEnglish (US)
Article number2052010
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume34
Issue number13
DOIs
StatePublished - Dec 15 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Deep learning
  • bee wings
  • convolutional neural network
  • ecology
  • image augmentation
  • image classification
  • inception residual module

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