RNN-LSTM: From applications to modeling techniques and …
Jun 1, 2024 · LSTM has been specifically designed to address the issue of vanishing gradients, which makes vanilla RNNs unsuitable for learning long-term dependencies (Jaydip and Sidra, …
Towards the next generation of Geospatial Artificial Intelligence
Feb 1, 2025 · Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directi…
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 …
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 …
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 …
Deep learning: RNNs and LSTM - ScienceDirect
LSTM and GRUs give exponentially many paths between times T and T − τ, with one path inhibited by neither linear transformations nor nonlinearities. We first remark that many …
Non-linear system modeling using LSTM neural networks
Jan 1, 2018 · Long-Short Term Memory (LSTM) is a type of Recurrent Neural Networks (RNN). It takes sequences of information and uses recurrent mechanisms and gate t…
LSTM-ARIMA as a hybrid approach in algorithmic investment …
Jun 23, 2025 · This study makes a significant contribution to the growing field of hybrid financial forecasting models by integrating LSTM and ARIMA into a novel algorithmic investment …
A survey on anomaly detection for technical systems using LSTM …
Oct 1, 2021 · However, due to the recent emergence of different LSTM approaches that are widely used for different anomaly detection purposes, the present paper aims to present a …
LSTM networks based on attention ordered neurons for gear …
Nov 1, 2020 · LSTM-based methods for RUL prediction have been widely used in the industrial field, including turbofan engine [13], gear [14], bearing [15], lithium battery [16], tool [17] and …