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[HTML][HTML] A new hybrid based on long short-term memory network with spotted hyena optimization algorithm for multi-label text classification
An essential work in natural language processing is the Multi-Label Text Classification
(MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional …
(MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional …
SHO-CNN: A metaheuristic optimization of a convolutional neural network for multi-label news classification
News media always pursue informing the public at large. It is impossible to overestimate the
significance of understanding the semantics of news coverage. Traditionally, a news text is …
significance of understanding the semantics of news coverage. Traditionally, a news text is …
Selection of key features for PM2. 5 prediction using a wavelet model and RBF-LSTM
PM2. 5 prediction has received much attention from researchers in recent years, as PM2. 5
has been proven to have a major impact on human health. High-precision PM2. 5 …
has been proven to have a major impact on human health. High-precision PM2. 5 …
Total solar irradiance during the last five centuries
The total solar irradiance (TSI) varies on timescales of minutes to centuries. On short
timescales it varies due to the superposition of intensity fluctuations produced by turbulent …
timescales it varies due to the superposition of intensity fluctuations produced by turbulent …
[HTML][HTML] An improved VMD–EEMD–LSTM time series hybrid prediction model for sea surface height derived from satellite altimetry data
Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics,
making traditional time series forecasting methods less effective in producing satisfactory …
making traditional time series forecasting methods less effective in producing satisfactory …
Predicting Solar cycle 25 using an optimized long short-term memory model based on sunspot area data
H Zhu, H Chen, W Zhu, M He - Advances in Space Research, 2023 - Elsevier
In this paper, an optimized long short-term memory (LSTM) model was proposed to deal with
the monthly sunspot area (SSA) data, aiming to predict the peak amplitude of SSA and the …
the monthly sunspot area (SSA) data, aiming to predict the peak amplitude of SSA and the …
Prediction of solar cycle 25 using deep learning based long short-term memory forecasting technique
In the current work we have used the deep learning based long short-term memory model to
predict the strength and peak time of solar cycle 25 by employing the monthly smoothed …
predict the strength and peak time of solar cycle 25 by employing the monthly smoothed …
ECG-based heartbeat classification using exponential-political optimizer trained deep learning for arrhythmia detection
An electrocardiogram (ECG) computes the electrical functioning of the heart, which is mostly
employed for finding various heart diseases of its feasibility and simplicity. Moreover, some …
employed for finding various heart diseases of its feasibility and simplicity. Moreover, some …
Electricity price forecast based on the STL-TCN-NBEATS model
Taking long-term high-frequency electricity price data as the research content, this paper
proposes seasonal and trend decomposition using loess-temporal convolutional network …
proposes seasonal and trend decomposition using loess-temporal convolutional network …
Prediction of reference crop evapotranspiration based on improved convolutional neural network (CNN) and long short-term memory network (LSTM) models in …
M Li, Q Zhou, X Han, P Lv - Journal of Hydrology, 2024 - Elsevier
The accurate prediction of reference crop evapotranspiration (ET 0) is essential to better
manage crop irrigation water consumption and improve crop water use efficiency. To …
manage crop irrigation water consumption and improve crop water use efficiency. To …