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Redes neurais recorrentes lstm e suas aplicações
(Universidade Federal de Santa MariaBrasilUFSMCentro de Tecnologia, 2018-08-10)
The objective of this work is to review the Long Short-Term Memory (LSTM) artificial neural network model and its application in different activities, in order to evaluate its versatility. Four activities are presented, ...
Prediction of imports of household appliances in Ecuador using LSTM networks
(Springer Nature Switzerland AG 2020, 2020)
Time series forecasting is an important topic widely addressed with traditional statistical models such as regression, and moving average. This work uses the state-of-the-art Long Short-Term Memory (LSTM) Networks to predict ...
Applied LSTM neural network time series to forecast household energy consumption
(SCOPUS, 2021-07)
In Ecuador, energy consumption is accentuated in
the residential sector due to population growth and other
parameters, which leads to an increase in energy costs,
greenhouse gas emissions and fossil fuel subsidies. ...
Predicting customer abandonment in recurrent neural networks using short-term memory
(Elsevier, 2023)
Customer retention, a critical business priority, has become a growing concern, especially in the telecommunications industry. This study addresses the need to anticipate and understand customer churn through the application ...
LSTM deep neural networks postfiltering for enhancing synthetic voices
(2018)
Recent developments in speech synthesis have produced systems capable of producing speech which closely resembles natural speech, and researchers now strive to create models that more accurately mimic human voices. One ...
LSTM deep neural networks postfiltering for improving the quality of synthetic voices
(2016)
Recent developments in speech synthesis have produced systems capable of providing intelligible speech, and researchers now strive to create models that more accurately mimic human voices. One such development is the ...
Ensemble of temporal convolutional and long short-term memory neural networks apply to forecasting USDCOP exchange rate
(Universidad EAFITMaestría en Ciencias de los Datos y AnalíticaEscuela de AdministraciónMedellín, 2021)
This paper applies a neural network with ensemble of temporal convolutional network (TCN) and long short-term memory (LSTM) layers approach to forecast foreign exchange rates between the US dollar (USD) and Colombian Peso ...