An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

2021 ◽  
Vol 193 (12) ◽  
Author(s):  
Salar Valizadeh Moghadam ◽  
Ahmad Sharafati ◽  
Hajar Feizi ◽  
Seyed Mohammad Saeid Marjaie ◽  
Seyed Babak Haji Seyed Asadollah ◽  
...  
Keyword(s):  
Neural Network ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Dissolved Oxygen Concentration ◽  
Efficient Strategy ◽  
Deep Recurrent Neural Network

scholarly journals Blockchain-enabled Deep Recurrent Neural Network Model for Clickbait Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Abdul Razaque ◽  
Bandar Alotaibi ◽  
Munif Alotaibi ◽  
Fathi Amsaad ◽  
Ansagan Manasov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Deep Recurrent Neural Network

scholarly journals Study on the prediction of stock price based on the associated network model of LSTM

2019 ◽  
Vol 11 (6) ◽  
pp. 1307-1317 ◽  
Author(s):  
Guangyu Ding ◽  
Liangxi Qin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Stock Market ◽  
Statistical Methods ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Stock Price ◽  
Artificial Intelligence Methods ◽  
Deep Recurrent Neural Network

AbstractStock market has received widespread attention from investors. It has always been a hot spot for investors and investment companies to grasp the change regularity of the stock market and predict its trend. Currently, there are many methods for stock price prediction. The prediction methods can be roughly divided into two categories: statistical methods and artificial intelligence methods. Statistical methods include logistic regression model, ARCH model, etc. Artificial intelligence methods include multi-layer perceptron, convolutional neural network, naive Bayes network, back propagation network, single-layer LSTM, support vector machine, recurrent neural network, etc. But these studies predict only one single value. In order to predict multiple values in one model, it need to design a model which can handle multiple inputs and produces multiple associated output values at the same time. For this purpose, it is proposed an associated deep recurrent neural network model with multiple inputs and multiple outputs based on long short-term memory network. The associated network model can predict the opening price, the lowest price and the highest price of a stock simultaneously. The associated network model was compared with LSTM network model and deep recurrent neural network model. The experiments show that the accuracy of the associated model is superior to the other two models in predicting multiple values at the same time, and its prediction accuracy is over 95%.

Control of dissolved oxygen concentration using neural network in a batch bioreactor

2010 ◽  
Vol 20 (4) ◽  
pp. 619-628 ◽  
Author(s):  
Tufan Mete ◽  
Gulay Ozkan ◽  
Hale Hapoglu ◽  
Mustafa Alpbaz
Keyword(s):  
Neural Network ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Dissolved Oxygen Concentration ◽  
Batch Bioreactor

Data-driven recurrent neural network model to predict the rate of penetration

2021 ◽  
Vol 7 ◽  
pp. 100047
Author(s):  
Husam H. Alkinani ◽  
Abo Taleb T. Al-Hameedi ◽  
Shari Dunn-Norman
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Data Driven ◽  
Rate Of Penetration
Sign in / Sign up

Export Citation Format

Share Document