Long Short Term Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network designed to process sequential data by learning long-term dependencies, enabling accurate predictions and classifications in various applications. Current research focuses on enhancing LSTM architectures, such as incorporating convolutional layers, attention mechanisms, and hybrid models combining LSTMs with other deep learning techniques like transformers or graph neural networks, to improve efficiency and accuracy. This work is significant because LSTMs are proving highly effective across diverse fields, from financial forecasting and environmental monitoring to medical image analysis and activity recognition, offering powerful tools for analyzing complex temporal data.

507papers

Papers - Page 28

April 6, 2022

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
Zhuangwei Shi, Yang Hu, Guangliang Mo, Jian Wu
Long Short Term Memory Attention Based XGBoost Model Hybrid Model Stock Price Prediction

April 5, 2022

Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models
Birendra Jha, Medha Atre, Ashwini Rao
Deep Learning Model Long Short Term Memory Phishing Attack Adversarial Cloud

April 4, 2022

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation
Christoph Wehner, Francis Powlesland, Bashar Altakrouri, Ute Schmid
Lane Change Digital Twin Long Short Term Memory Layer Wise Relevance Propagation Multi Layer

March 30, 2022

Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation
Yan Qin, Chau Yuen, Yimin Shao, Bo Qin, Xiaoli Li
Capsule Network Industrial Machine Useful Life Long Short Term Memory CapsNet Model

March 29, 2022

A single Long Short-Term Memory network for enhancing the prediction of path-dependent plasticity with material heterogeneity and anisotropy
Ehsan Motevali Haghighi, SeonHong Na
3D Microstructures Human Prediction Heterogeneous Material Path Dependent Dependent Plasticity Long Short Term Memory

March 28, 2022

March 23, 2022

Music Generation Using an LSTM
Michael Conner, Lucas Gral, Kevin Adams, David Hunger, Reagan Strelow, Alexander Neuwirth
Music Generation Recurrent Neural Network Long Short Term Memory

March 22, 2022

Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis
Daniel Hopp
System Performance Covid 19 Pandemic Long Short Term Memory Neural Network

March 21, 2022

Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning
Marc R. Schlichting, Stefan Notter, Walter Fichter
Policy Architecture Path Planning Spatial Encoding Multi Agent Path Planning Multi Agent System Long Short Term Memory Learning Based

March 19, 2022

March 18, 2022

Prediction of chaotic attractors in quasiperiodically forced logistic map using deep learning
J. Meiyazhagan, M. Senthilvelan
Human Prediction Deep Learning Chaotic Attractor Long Short Term Memory Chaotic Dynamic

March 15, 2022

Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction
Ge Gao, Farzaneh Khoshnevisan, Min Chi
Imputation Task Imputation Algorithm Information Spread Imputation Method Time Aware Long Short Term Memory

March 13, 2022

Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment
Nicole Koenigstein
Smoothed Classifier Financial Time Series Attention Module Context Aware Long Short Term Memory Recurrent Module News Sentiment Recurrent Structure

March 11, 2022

Multiple Inputs Neural Networks for Medicare fraud Detection
Mansour Zoubeirou A Mayaki, Michel Riveill
Medicare Fraud Fraud Detection Long Short Term Memory

March 10, 2022

Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization
Mufhumudzi Muthivhi, Terence L. van Zyl
Investor Sentiment Portfolio Optimization Implicit Sentiment Portfolio Optimization Problem Long Short Term Memory Portfolio Selection Hybrid Fusion

March 9, 2022

Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding
Victoria Bell1, Divish Rengasamy, Benjamin Rothwell, Grazziela P Figueredo
Anomaly Detection Stacked Autoencoder Long Short Term Memory UAV Data Outlier Detection UAV Dataset

March 8, 2022

LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data
Lianlian Jiang, Yuexuan Wang, Wenyi Zheng, Chao Jin, Zengxiang Li, Sin G. Teo
Sequential Data Long Short Term Memory Split Learning Neural Network Recurrent Neural Network

Long Short Term Memory

Papers - Page 28

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation

A single Long Short-Term Memory network for enhancing the prediction of path-dependent plasticity with material heterogeneity and anisotropy

Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy

A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction

Music Generation Using an LSTM

Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis

Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning

Machine Learning based Laser Failure Mode Detection

A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction

Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory

Prediction of chaotic attractors in quasiperiodically forced logistic map using deep learning

Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction

Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment

Multiple Inputs Neural Networks for Medicare fraud Detection

Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization

Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding

LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data