Long Short-Term Memory Network - an overview - ScienceDirect
Network LSTM refers to a type of Long Short-Term Memory (LSTM) network architecture that is particularly effective for learning from sequences of data, utilizing specialized structures and …
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 sequential data with long-term …
Long Short-Term Memory - an overview | ScienceDirect Topics
LSTM, or long short-term memory, is defined as a type of recurrent neural network (RNN) that utilizes a loop structure to process sequential data and retain long-term information through a …
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 …
LSTM-ARIMA as a hybrid approach in algorithmic investment …
Jun 23, 2025 · Abstract This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM …
PI-LSTM: Physics-informed long short-term memory
Oct 1, 2023 · The PI-LSTM network, inspired by and compared with existing physics-informed deep learning models (PhyCNN and PhyLSTM), was validated using the numerical simulation …
Enhancing streamflow forecasting using an LSTM hybrid model …
Consequently, LSTM attracts considerable attention and has been rigorously validated in hydrological forecasting. Chen et al. (2020) compared an artificial neural network (ANN) with …
Fundamentals of Recurrent Neural Network (RNN) and Long Short …
Mar 1, 2020 · Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blo…
Probing an LSTM-PPO-Based reinforcement learning algorithm to …
Nov 1, 2024 · This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more …
LSTM, WaveNet, and 2D CNN for nonlinear time history prediction …
Jul 1, 2023 · LSTM has been previously developed and is utilized to serve as a reference model, while WaveNet and 2D CNN (i.e., it deals with the data in coupled time–frequency dimensions) …