A Deep Learning Model with Long Short-Term Memory (DLSTM) for Prediction of Currency Exchange Rates

2020 ◽  
pp. 484-498
Author(s):  
Thitimanan Damrongsakmethee ◽  
Victor-Emil Neagoe
Keyword(s):  
Deep Learning ◽  
Exchange Rates ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Term Memory ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Long Short Term Memory ◽  
Deep Learning Model

Deep Learning Model for Forecasting Financial Sales Based on Long Short-Term Memory Networks

2019 ◽  
pp. 498-506
Author(s):  
Pablo F. Ordoñez-Ordoñez ◽  
Martha C. Suntaxi Sarango ◽  
Cristian Narváez ◽  
Maria del Cisne Ruilova Sánchez ◽  
Mario Enrique Cueva-Hurtado
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Deep Learning Model

scholarly journals Psychological Stress Detection According to ECG Using a Deep Learning Model with Attention Mechanism

2021 ◽  
Vol 11 (6) ◽  
pp. 2848
Author(s):  
Pengfei Zhang ◽  
Fenghua Li ◽  
Lidong Du ◽  
Rongjian Zhao ◽  
Xianxiang Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Attention Mechanism ◽  
Stress Detection ◽  
Short Term ◽  
Term Memory ◽  
Average Accuracy ◽  
Long Short Term Memory ◽  
Deep Learning Model

To satisfy the need to accurately monitor emotional stress, this paper explores the effectiveness of the attention mechanism based on the deep learning model CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) As different attention mechanisms can cause the framework to focus on different positions of the feature map, this discussion adds attention mechanisms to the CNN layer and the BiLSTM layer separately, and to both the CNN layer and BiLSTM layer simultaneously to generate different CNN–BiLSTM networks with attention mechanisms. ECG (electrocardiogram) data from 34 subjects were collected on the server platform created by the Institute of Psychology of the Chinese Academy of Science and the researches. It verifies that the average accuracy of CNN–BiLSTM is up to 0.865 without any attention mechanism, while the highest average accuracy of 0.868 is achieved using the CNN–attention–based BiLSTM.

scholarly journals Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 924
Author(s):  
Moslem Imani ◽  
Hoda Fakour ◽  
Wen-Hau Lan ◽  
Huan-Chin Kao ◽  
Chi Ming Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Long Short Term Memory ◽  
Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

scholarly journals Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

2021 ◽  
Vol 9 ◽  
Author(s):  
R. Lakshmana Kumar ◽  
Firoz Khan ◽  
Sadia Din ◽  
Shahab S. Band ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Real World Data ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Learning Reinforcement ◽  
Novel Coronavirus

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.

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