IEEE

LSTM Hardware Inference Accelerator for LiteRT

Abstract: The efficient deployment of Recurrent Neural Networks (RNNs), particularly long short-term memory (LSTM) architectures, on edge devices has become increasingly important due to their ability ...
Frontiers

Image steganalysis using LSTM fused convolutional neural networks for secure telemedicine

Deep learning-based image steganalysis has progressed in recent times, with efforts more concerted toward prioritizing detection accuracy over lightweight frameworks. In the context of AI-driven ...
marktechpost

Deep Learning Framework Showdown: PyTorch vs TensorFlow in 2025

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...
Scientific Research Publishing

Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning Approach ()

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...
Frontiers

Accurate earthquake and mining tremor identification via a CEEMDAN-LSTM framework

The accurate identification of mining tremors and earthquakes is important for establishing a comprehensive mining tremor catalog that can aid in providing regulatory oversight for mining activities.
GitHub

build-an-lstm-model-with-tensorflow-and-keras.md

Long Short-Term Memory (LSTM) based neural networks have played an important role in the field of Natural Language Processing. In addition, they have been used widely for sequence modeling. The reason ...
IEEE

LSTM Neural Network Intelligent Power Load Based on TensorFlow

Abstract: With the growth of global energy demand and the widespread use of renewable energy, how to accurately predict power demand has become a key issue to ensure the stability of the power grid ...