Results 171 to 180 of about 439,227 (216)

An enhanced adaptive dynamic metaheuristic optimization algorithm for rainfall prediction depends on long short-term memory. [PDF]

PLoS One
Elshewey AM, Alhussan AA, Khafaga DS, Radwan M, El-Kenawy EM, Khodadadi N.   +5 more
europepmc   +1 more source

Music audio emotion regression using the fusion of convolutional neural networks and bidirectional long short-term memory models. [PDF]

PeerJ Comput Sci
Qiu Y, Lin Y, Lin Y.
europepmc   +1 more source

Macroeconomic-aware forecasting of construction costs in developing countries: Using gated recurrent unit and long short-term memory deep learning framework. [PDF]

PLoS One
Alzara M, Gihad N, Abdou H, Soltan A, Hilal A, Ehab A, Ehab A.   +6 more
europepmc   +1 more source

Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature. [PDF]

Sci Data
Sun M, Meng Q, Zhang L, Hu X, Lei X, Chen S, Hou J.   +6 more
europepmc   +1 more source

Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms

Applied Energy, 2019
This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Network for pattern recognition with the Long Short-Term Memory Network for half-hourly global solar radiation (GSR) forecasting.
Sujan Ghimire, Ravinesh C Deo, Nawin Raj   +2 more
exaly   +2 more sources

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A Modified Long Short-Term Memory Cell

International Journal of Neural Systems, 2023
Machine Learning (ML), among other things, facilitates Text Classification, the task of assigning classes to textual items. Classification performance in ML has been significantly improved due to recent developments, including the rise of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Transformer ...
Giannis Haralabopoulos, Gerasimos Razis, Ioannis Anagnostopoulos   +2 more
openaire   +2 more sources

Long Short-Term Memory

Neural Computation, 1997
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating
Hochreiter, Sepp, Schmidhuber, Jürgen
openaire   +3 more sources

A review on the long short-term memory model

Artificial Intelligence Review, 2020
Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google’s speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon’s Alexa.
Greg Van Houdt, Carlos Mosquera, Gonzalo Nápoles   +2 more
openaire   +1 more source

Long short-term memory with activation on gradient

Neural Networks, 2023
As the number of long short-term memory (LSTM) layers increases, vanishing/exploding gradient problems exacerbate and have a negative impact on the performance of the LSTM. In addition, the ill-conditioned problem occurs in the training process of LSTM and adversely affects its convergence.
Chuan Qin, Liangming Chen, Zangtai Cai, Mei Liu, Long Jin 0001   +4 more
openaire   +2 more sources