Journal ArticleDOI
Parking slot occupancy prediction using LSTM
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This article is published in Innovations in Systems and Software Engineering.The article was published on 2022-09-10. It has received 1 citations till now. The article focuses on the topics: Occupancy & Computer science.read more
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A Novel Fault Diagnosis Method Based on SWT and VGG-LSTM Model for Hydraulic Axial Piston Pump
TL;DR: In this paper , the authors integrated the time-frequency feature conversion capability of synchrosqueezing wavelet transform (SWT), the feature extraction capability of VGG11, as well as the feature memory capability of the long short-term memory (LSTM) model.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
Cort J. Willmott,Kenji Matsuura +1 more
TL;DR: In this paper, the root-mean-square error (RMSE) and the mean absolute error (MAE) were examined to describe average model-performance error, and it was shown that MAE is a more natural measure of average error than RMSE.
Proceedings ArticleDOI
A Comparison of ARIMA and LSTM in Forecasting Time Series
TL;DR: The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithm such as ARIMA model and the average reduction in error rates obtained by L STM was between 84 - 87 percent when compared to ARimA indicating the superiority of LSTm to AR IMA.
Journal ArticleDOI
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
TL;DR: In this paper, a Long Short-Term Memory (LSTM) neural network model was used for flood forecasting, where the daily discharge and rainfall were used as input data, and characteristics of the data sets which may influence the model performance were also of interest.
Proceedings ArticleDOI
A comparative study of RNN for outlier detection in data mining
TL;DR: This work compares RNN for outlier detection with three other methods using both publicly available statistical datasets and data mining datasets to provide insights into the relative strengths and weaknesses of RNNs.