Page 199 - 46-1

วารสารราชบััณฑิิตยสภา
ปีีที่่� ๔๖ ฉบัับัที่่� ๑ มกราคม-เมษายน ๒๕๖๔
ศาสตราจารย์์ ดร.ธนารักษ์์ ธีระมั่ั�นคง และนางสาวเบญจพรรณ สมั่ณะ 191

Representations 2015. San Diego, CA. [online]. from https://arxiv.org/abs/

1409.1556.

Sriapha, Charuwan & Tongpoo, Achara & Wongvisavakorn, Sunun & Rittilert, Panee &
Trakulsrichai, Satariya & Srisuma, Sahaphume & Wananukul, Winai. (2015).
Plant Poisoning in Thailand: A 10-year Analysis from Ramathibodi Poison Center.

The Southeast Asian journal of tropical medicine and public health.

46. 1063–76.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. (2014). Dropout:
A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine

Learning Research. 15, 1929-1958. [online]. from http://jmlr.org/papers/v15/

srivastava14a.html.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. & Anguelov, D. et al. (2015). Going
deeper with convolutions. In 2015 IEEE Conference on Computer Vision and

Pattern Recognition (CVPR). Boston, MA, USA: IEEE. [online]. from https://

ieeexplore.ieee.org/document/7298594.
Tan, M. & V. Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional
Neural Networks. In Thirty-sixth International Conference on Machine

Learning (ICML 2019). Long Beach, California, USA.

Thye Hang, S., & Aono, M. (2019). Open world plant image identification based on
convolutional neural network. In 2016 Asia-Pacific Signal and Information
Processing Association Annual Summit and Conference (APSIPA). Jeju,

South Korea: IEEE. [online]. from https://ieeexplore.ieee.org/document/7820676

University of British Columbia. (2000). Method and apparatus for identifying
scale invariant features in an image and use of same for locating an object in
an image. US.

Zhang, C., Zou, P., Li, C. & Liu, L. (2015). A Convolutional Neural Network for Leaves

Recognition Using Data Augmentation. In 2015 IEEE International Conference