|
|
|
|
|
Yong Liu, Xiaohui Yan, Wenying Du, Tianqi Zhang, Xiaopeng Bai and Ruichuan Nan
The current work proposes a novel super-resolution convolutional transposed network (SRCTN) deep learning architecture for downscaling daily climatic variables. The algorithm was established based on a super-resolution convolutional neural network with t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Alvaro A. Teran-Quezada, Victor Lopez-Cabrera, Jose Carlos Rangel and Javier E. Sanchez-Galan
Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long?short-term memory (LSTM) have become a means for providing solutions to problem...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ancilon Leuch Alencar, Marcelo Dornbusch Lopes, Anita Maria da Rocha Fernandes, Julio Cesar Santos dos Anjos, Juan Francisco De Paz Santana and Valderi Reis Quietinho Leithardt
In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existin...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ching-Lung Fan
The emergence of deep learning-based classification methods has led to considerable advancements and remarkable performance in image recognition. This study introduces the Multiscale Feature Convolutional Neural Network (MSFCNN) for the extraction of com...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shaoyan Zuo, Dazhi Wang, Xiao Wang, Liujia Suo, Shuaiwu Liu, Yongqing Zhao and Dewang Liu
In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (????
H
s
) and wave period (????
T
s
) from X-band marine radar images. Since the shore-based radar image in this study is in...
ver más
|
|
|
|
|
|
|
|
|
|
|
Diya Wang, Yonglin Zhang, Lixin Wu, Yupeng Tai, Haibin Wang, Jun Wang, Fabrice Meriaudeau and Fan Yang
In recent years, the study of deep learning techniques for underwater acoustic channel estimation has gained widespread attention. However, existing neural network channel estimation methods often overfit to training dataset noise levels, leading to dimi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybri...
ver más
|
|
|
|
|
|
|
|
|
|
|
Oscar Leonardo García-Navarrete, Oscar Santamaria, Pablo Martín-Ramos, Miguel Ángel Valenzuela-Mahecha and Luis Manuel Navas-Gracia
Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ying Chen, Xi Qiao, Feng Qin, Hongtao Huang, Bo Liu, Zaiyuan Li, Conghui Liu, Quan Wang, Fanghao Wan, Wanqiang Qian and Yiqi Huang
Invasive plant species pose significant biodiversity and ecosystem threats. Real-time identification of invasive plants is a crucial prerequisite for early and timely prevention. While deep learning has shown promising results in plant recognition, the u...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zhiqing Guo, Xiaohui Chen, Ming Li, Yucheng Chi and Dongyuan Shi
Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction me...
ver más
|
|
|
|
|
|