A survey on long short-term memory networks for time series …
Jan 1, 2021 · Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear …
RNN-LSTM: From applications to modeling techniques and …
Jun 1, 2024 · Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN) algorithm known for its ability to effectively analyze and process sequentia…
Working Memory Connections for LSTM - ScienceDirect
Dec 1, 2021 · For the sMNIST task, peephole LSTM performs slightly better than vanilla LSTM. LSTM with Working Memory Connections, instead, outperforms the competing architectures in …
Long Short-Term Memory Network - an overview - ScienceDirect
Jul 7, 2020 · A Long Short-Term Memory Network, also known as LSTM, is an advanced recurrent neural network that uses "gates" to capture both long-term and short-term memory. …
LSTM-FKAN coupled with feature extraction technique for …
May 1, 2025 · The soil characteristic data is represented by root zone soil moisture, which is derived from raster data. The LSTM-FKAN coupled with feature extraction technique …
Development of a CNN-LSTM Approach with Images as Time …
Jan 1, 2024 · In addition, this CNN-LSTM model is enriched with input in the form of images that represent the timeseries data, where the gramian angular field (GAF) technique is used in …
Interpretable spatio-temporal attention LSTM model for flood ...
Aug 25, 2020 · LSTM-based methods Long Short Term Memory (LSTM) is a modified version of recurrent neural networks, which is proposed to solve the problem of long-distance (time) …
Fundamentals of Recurrent Neural Network (RNN) and Long Short …
Mar 1, 2020 · All major open source machine learning frameworks offer efficient, production-ready implementations of a number of RNN and LSTM network architectures. Naturally, some …
A multi-source transfer learning model based on LSTM and …
Jul 1, 2023 · The model integrated multiple subnetworks, and LSTM extracts the temporal features of each pair of source–target buildings separately. The distribution distance between …
Improving the explainability of CNN-LSTM-based flood prediction …
Dec 1, 2024 · The results show that the coupled CNN-LSTM model performs better than the flood predictions compared to the individual CNN or LSTM models under the longest foresight …