81‐2: Invited Paper: Neural Network Based Quantitative Evaluation of Display Non‐Uniformity Corresponds Well with Human Visual Evaluation

2020 ◽  
Vol 51 (1) ◽  
pp. 1214-1217
Author(s):  
Kazuki Tsutsukawa ◽  
Manabu Kobayashi ◽  
Yusuke Bamba
Keyword(s):  
Neural Network ◽  
Quantitative Evaluation ◽  
Visual Evaluation

scholarly journals Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Wang ◽  
Moyang Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Information Extraction ◽  
Spatial Scale ◽  
Quantitative Evaluation ◽  
Urban Landscape ◽  
Kappa Coefficient ◽  
Deep Convolutional Neural Network ◽  
Landscape Information ◽  
Extraction Model

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

scholarly journals Quantitative Evaluation of Hand Washing Skills Based on Convolutional Neural Network for Nursing Education

Forma ◽  
2020 ◽  
Vol 35 (1) ◽  
pp. 15-19
Author(s):  
Kazushi Yamamoto ◽  
Miho Yoshii ◽  
Fumiya Kinoshita ◽  
Hideaki Touyama
Keyword(s):  
Neural Network ◽  
Nursing Education ◽  
Convolutional Neural Network ◽  
Quantitative Evaluation ◽  
Hand Washing
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