scholarly journals Wafer Edge Yield Prediction Using a Combined Long Short-Term Memory and Feed- Forward Neural Network Model for Semiconductor Manufacturing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 215125-215132
Author(s):  
Dasol Kim ◽  
Mintae Kim ◽  
Wooju Kim
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Semiconductor Manufacturing ◽  
Short Term Memory ◽  
Yield Prediction ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Wafer Edge

scholarly journals Supervised Mover's Distance: A simple model for sentence comparison

2018 ◽  
Author(s):  
Muktabh Mayank Srivastava
Keyword(s):  
Neural Network ◽  
Simple Model ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Network Module ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Simple Neural Network

We propose a simple neural network model which can learn relation between sentences by passing their representations obtained from Long Short Term Memory(LSTM) through a Relation Network. The Relation Network module tries to extract similarity between multiple contextual representations obtained from LSTM. Our model is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two sentence comparison datasets.

scholarly journals Earthquake trend prediction using long short-term memory RNN

2019 ◽  
Vol 9 (2) ◽  
pp. 1304 ◽  
Author(s):  
Tanvi Bhandarkar ◽  
Vardaan K ◽  
Nikhil Satish ◽  
S. Sridhar ◽  
R. Sivakumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Future Trend ◽  
Earthquake Forecasting ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Trend Prediction ◽  
Term Memory ◽  
Area Of Interest ◽  
Long Short Term Memory

<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.</p>

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