Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks

2015 ◽  
Author(s):  
Tara N. Sainath ◽  
Oriol Vinyals ◽  
Andrew Senior ◽  
Hasim Sak
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Fully Connected

scholarly journals Using Machine Learning Methods in the Financial Market for Technical Analysis Based on Hybrid Models

2020 ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Input Structure ◽  
Long Short Term Memory

In this study, it is presented a new hybrid model based on deep neural networks to predict the direction and magnitude of the Forex market movement in the short term. The overall model presented is based on the scalping strategy and is provided for high frequency transactions. The proposed hybrid model is based on a combination of three models based on deep neural networks. The first model is a deep neural network with a multi-input structure consisting of a combination of Long Short Term Memory layers. The second model is a deep neural network with a multi-input structure made of a combination of one-dimensional Convolutional Neural network layers. The third model has a simpler structure and is a multi-input model of the Multi-Layer Perceptron layers. The overall model was also a model based on the majority vote of three top models. This study showed that models based on Long Short-Term Memory layers provided better results than the other models and even hybrid models with more than 70% accurate.

scholarly journals Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

2021 ◽  
Vol 11 (24) ◽  
pp. 12019
Author(s):  
Chia-Chun Chuang ◽  
Chien-Ching Lee ◽  
Chia-Hong Yeng ◽  
Edmund-Cheung So ◽  
Yeou-Jiunn Chen
Keyword(s):  
Blood Pressure ◽  
Neural Networks ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Blood Pressure Estimation ◽  
Fully Connected ◽  
Electrocardiogram Ecg

Monitoring people’s blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard.

scholarly journals Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances

2020 ◽  
Vol 12 (11) ◽  
pp. 4471 ◽  
Author(s):  
Jack Ngarambe ◽  
Amina Irakoze ◽  
Geun Young Yun ◽  
Gon Kim
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Indoor Spaces

The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the potential applications of ML algorithms in modeling daylight in indoor spaces and ultimately identify the optimum algorithm. We thus developed and compared the performance of four common ML algorithms: generalized linear models, deep neural networks, random forest, and gradient boosting models in predicting the distribution of indoor daylight illuminances. We found that deep neural networks, which showed a determination of coefficient (R2) of 0.99, outperformed the other algorithms. Additionally, we explored the use of long short-term memory to forecast the distribution of daylight at a particular future time. Our results show that long short-term memory is accurate and reliable (R2 = 0.92). Our findings provide a basis for discussions on ML algorithms’ use in modeling daylight in indoor spaces, which may ultimately result in efficient tools for estimating daylight performance in the primary stages of building design and daylight control schemes for energy efficiency.

scholarly journals Application of Deep Learning Methods to Forecasting Changes in Short-Term Currency Trends

2020 ◽  
Vol 7 (2) ◽  
pp. 75-86
Author(s):  
Vasily D. Derbentsev ◽  
Vitalii S. Bezkorovainyi ◽  
Iryna V. Luniak
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Better Than

This study investigates the issues of forecasting changes in short-term currency trends using deep learning models, which is relevant for both the scientific community and for traders and investors. The purpose of this study is to build a model for forecasting the direction of change in the prices of currency quotes based on deep neural networks. The developed architecture was based on the model of valve recurrent node, which is a modification of the model of “Long Short-Term Memory”, but is simpler in terms of the number of parameters and learning time. The forecast calculations of the dynamics of quotations of the currency pair euro/dollar and the most capitalised cryptocurrency Bitcoin/dollar were performed using daily, four-hour and hourly datasets. The obtained results of binary classification (forecast of the direction of trend change) when applying daily and hourly quotations turned out to be generally better than those of time series models or models of neural networks of other architecture (in particular, multilayer perceptron or “Long Short-Term Memory” models). According to the study results, the highest accuracy of classification was for the model of daily quotations for both euro/dollar – about 72%, and for Bitcoin/ dollar – about 69%. For four-hour and hourly time series, the accuracy of classification decreased, which can be explained both by the increase in the impact of “market noise” and the probable overfitting. Computer simulation has demonstrated that models predict a rising trend better than a declining one. The study confirmed the prospects for the application of deep learning models for short-term forecasting of time series of currency quotes. The use of the developed models proved to be effective for both fiat and cryptocurrencies. The proposed system of models based on deep neural networks can be used as a basis for developing an automated trading system in the foreign exchange market

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