LSTM Network
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network designed to process sequential data, excelling at tasks requiring the retention of long-term dependencies. Current research focuses on improving LSTM performance through hybrid architectures (e.g., combining LSTMs with Convolutional Neural Networks or Transformers), optimizing model parameters, and addressing challenges like overfitting and computational cost in diverse applications. LSTMs' ability to model temporal patterns makes them valuable tools across numerous fields, including time series forecasting (weather, finance, energy), natural language processing, and signal processing, leading to advancements in areas like predictive maintenance and healthcare.
Papers
Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM
Majid Ali, Hina Shakir, Asia Samreen, Sohaib Ahmed
Learning Sign Language Representation using CNN LSTM, 3DCNN, CNN RNN LSTM and CCN TD
Nikita Louison, Wayne Goodridge, Koffka Khan