Accumulating confidence for deep neural network object detections and semantic segmentations in sequential UAS imagery through spatiotemporal feature correspondences generated from SfM techniques

2021 ◽  
Author(s):  
Trevor M. Bajkowski ◽  
James Alex Hurt ◽  
David Huangal ◽  
Jeffrey Dale ◽  
James Keller ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Spatiotemporal Feature ◽  
Feature Correspondences ◽  
Network Object

scholarly journals Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yamin Shen ◽  
Ping-Huan Kuo ◽  
Yung-Hsiang Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
The Other ◽  
Percentage Error ◽  
Model Assessment ◽  
Absolute Percentage Error ◽  
Spatiotemporal Feature ◽  
The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

Effect of Label Noise on Robustness of Deep Neural Network Object Detectors

2021 ◽  
pp. 239-250
Author(s):  
Bishwo Adhikari ◽  
Jukka Peltomäki ◽  
Saeed Bakhshi Germi ◽  
Esa Rahtu ◽  
Heikki Huttunen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Label Noise ◽  
Network Object

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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