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Nov 14, 2023 ¡¤ This study builds on previous research focused on the application of artificial neural networks to achieve accurate electrical load forecasting.
The objective of the present work is to compare two Deep Learning models, namely the Long Short-Term Memory (LSTM) model, and the Bi-directional LSTM (BLSTM)
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The aim of this study was to develop a bidirectional long short-term memory (BiLSTM) model—TOP-Net—that is applicable to both intensive care units and general ...
Apr 15, 2021 ¡¤ Conclusions: TOP-Net is an early tachycardia prediction model that uses 8 types of data from wearable sensors and electronic health records.
This paper aims to propose a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture
Bidirectional LSTM, on the other hand, can effectively combine past and future data for prediction, extracting key information, thereby improving the accuracy ...
To solve these problems, a hybrid model combining convolutional neural network and bidirectional long and short term memory network was adopted in this study.
Jun 19, 2024 ¡¤ This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory.
Jul 10, 2024 ¡¤ Optimal production planning based on accurate market demand forecasting is crucial for cost control, inventory.
The CNN extraction by using layers the relevant features from the input data and reduce its dimensionality, while the BLSTM layers learn temporal dependencies ...