scholarly journals A Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm Detection

2021 ◽  
Vol 17 (11) ◽  
pp. 1093-1098
Author(s):  
Mohammed M. AL-Ani ◽  
Nazlia Omar ◽  
Ahmed Adil Nafea
Keyword(s):  
Hybrid Method ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems

2020 ◽  
pp. 147592172093281
Author(s):  
Linchao Li ◽  
Haijun Zhou ◽  
Hanlin Liu ◽  
Chaodong Zhang ◽  
Junhui Liu
Keyword(s):  
Missing Data ◽  
Empirical Mode Decomposition ◽  
Hybrid Method ◽  
Short Term Memory ◽  
Measured Signal ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Memory Network ◽  
Long Short Term Memory

Missing data, especially a block of missing data, inevitably occur in structural health monitoring systems. Because of their severe negative effects, many methods that use measured data to infer missing data have been proposed in previous research to solve the problem. However, capturing complex correlations from raw measured signal data remains a challenge. In this study, empirical mode decomposition is combined with a long short-term memory deep learning network for the recovery of the measured signal data. The proposed hybrid method converts the missing data imputation task as a time series prediction task, which is then solved by a “divide and conquer” strategy. The core concept of this strategy is the prediction of the subsequences of the raw measured signal data, which are decomposed by empirical mode decomposition rather than directly predicted, as the decomposition can assist in the modeling of the irregular periodic changes of the measured signal data. In addition, the long short-term memory network in the hybrid model can remember more long-range correlations of subsequences than can the traditional artificial neural network. Three widely used prediction models, namely, the autoregressive integrated moving average, support vector regression, and artificial neural network models, are also implemented as benchmark models. Raw acceleration data collected from a cable-stayed bridge are used to evaluate the performance of the proposed method for missing measured signal data imputation. The recovery results of the measured signal data demonstrate that the proposed hybrid method exhibits excellent performance from two perspectives. First, the decomposition by empirical mode decomposition can improve the accuracy of the core long short-term memory prediction model. Second, the long short-term memory model outperforms other benchmark models because it can fit more microscopic changes of measured values. The experiments conducted in this study also suggest that the change patterns of raw measured signal data are complex, and it is therefore important to extract the features before modeling.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

Sign in / Sign up

Export Citation Format

Share Document