Verification of the technical equipment degradation method using a hybrid reinforcement learning trees–artificial neural network system

2021 ◽  
Vol 153 ◽  
pp. 106618
Author(s):  
Jakub Gajewski ◽  
David Vališ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Network System ◽  
Technical Equipment ◽  
Neural Network System ◽  
Artificial Neural ◽  
Hybrid Reinforcement

Early diagnosis of melanoma using an artificial neural network system

1997 ◽  
Vol 7 (Supplement 1) ◽  
pp. S58 ◽  
Author(s):  
M Burroni ◽  
G Dell??Eva ◽  
P Puddu ◽  
F Atzori ◽  
R Bono ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Diagnosis ◽  
Network System ◽  
Neural Network System ◽  
Artificial Neural

scholarly journals Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project

2013 ◽  
Vol 3 (1) ◽  
pp. 83 ◽  
Author(s):  
Kenichi Nakajima ◽  
Yasuo Nakajima ◽  
Hiroyuki Horikoshi ◽  
Munehisa Ueno ◽  
Hiroshi Wakabayashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diagnostic Accuracy ◽  
Bone Scan ◽  
Network System ◽  
System A ◽  
Neural Network System ◽  
Artificial Neural

Application of Artificial Neural Network for Cotton Boil Spoiling Disease Prediction

2010 ◽  
Vol 143-144 ◽  
pp. 233-237
Author(s):  
Fu Gui Chen ◽  
Bao Jian Zhang ◽  
Jun Hui Fu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Disease Prediction ◽  
Network System ◽  
Neural Network System ◽  
Average Quantity ◽  
Artificial Neural ◽  
High Output

Based on the database of cotton boil spoiling disease in Xinxiang, a computerized intelligent expert system was established by using the Reverse Model of artificial neural network. With its speediness, robustness and 100%predicting accuracy, the system can be used as an effective method to predict the trend of cotton diseases. In recent years, we have seem some reports for which use artificial neural network system to forecast the disease of crops, but the artificial neural network using for predicting cotton boil spoiling disease have not been seen yet. Xinxiang is a city of Henan province of china, according to the survey materials of 10 years, the high output cotton boil spoiling disease break out every 4 years, the average quantity is 1.53, the rate of boil spoiling disease is 11.84%, so the loss is 168.28 . In order to prevent the cotton boil spoiling disease, we should forecast the disease, by doing this, it can increase quantity and quality of the cotton.

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