Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic

2018 ◽  
Vol 16 (7) ◽  
pp. 3829-3838 ◽  
Author(s):  
F. Arabi Aliabad ◽  
S. Shojaei ◽  
M. Zare ◽  
M. R. Ekhtesasi
Keyword(s):  
Neural Network ◽  
Satellite Images ◽  
Geological Mapping ◽  
Boolean Logic ◽  
Fuzzy Artmap ◽  
Method Performance ◽  
Neural Network Method ◽  
Network Method

scholarly journals Automatic Classification of Pararubber Trees in Thailand from LANDSAT-8 Images Using Neural Network Method

2021 ◽  
Author(s):  
C. Supunyachotsakul ◽  
N. Suksangpanya
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Ground Truth ◽  
Classification Model ◽  
Landsat 8 ◽  
Neural Network Method ◽  
Network Method ◽  
Convolutional Encoder

Classifying features from satellite images has been a time-consuming manual process which requires lots of manpower. This work exploits deep convolutional encoder-decoder neural network to develop an algorithm that can automatically classify the extents of the Pararubber tree-growing areas from the LANDSAT-8 images. The ground truth of the areas of the Pararubber tree was manually prepared and was separated into training datasets and the validation datasets. The classification model from this approach obtained using the training datasets was verified with the classification accuracy of70.90%, precision of 67.66%, recall of 80.80%, and F1 score of 73.59%.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

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