Deep Neural Network Classification of In Vivo Burn injuries with Different Etiologies Using THz Time-Domain Spectral Imaging

2021 ◽  
Author(s):  
Omar B. Osman ◽  
Zachery B. Harris ◽  
Juin-Wan Zhou ◽  
Mahmoud E. Khani ◽  
Andrew Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Deep Neural Network ◽  
Spectral Imaging ◽  
Burn Injuries ◽  
Neural Network Classification

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

Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

2021 ◽  
Author(s):  
Luke Gundry ◽  
Gareth Kennedy ◽  
Alan Bond ◽  
Jie Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reaction Mechanisms ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Neural Network Classification ◽  
Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

Deep Neural Network Classification of Tactile Materials Explored by Tactile Sensor Array With Various Active-Cell Formations

2020 ◽  
Vol 25 (4) ◽  
pp. 2134-2138
Author(s):  
Sung-Ho Lim ◽  
Kyungsoo Kim ◽  
Minkyung Sim ◽  
Kwonsik Shin ◽  
Doyoung Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Sensor Array ◽  
Tactile Sensor ◽  
Active Cell ◽  
Neural Network Classification ◽  
Tactile Sensor Array

Reduced lipid contamination in in vivo 1H MRSI using time-domain fitting and neural network classification

1993 ◽  
Vol 11 (7) ◽  
pp. 1019-1026 ◽  
Author(s):  
R. de Beer ◽  
F. Michels ◽  
D. van Ormondt ◽  
B.P.O. van Tongeren ◽  
P.R. Luyten ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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