[HTML][HTML] A new hybrid based on long short-term memory network with spotted hyena optimization algorithm for multi-label text classification

H Khataei Maragheh, FS Gharehchopogh… - Mathematics, 2022‏ - mdpi.com
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 …

SHO-CNN: A metaheuristic optimization of a convolutional neural network for multi-label news classification

MI Nadeem, K Ahmed, D Li, Z Zheng, H Naheed… - Electronics, 2022‏ - mdpi.com
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 …

Selection of key features for PM2. 5 prediction using a wavelet model and RBF-LSTM

YC Chen, DC Li - Applied Intelligence, 2021‏ - Springer
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 …

Total solar irradiance during the last five centuries

V Penza, F Berrilli, L Bertello, M Cantoresi… - The Astrophysical …, 2022‏ - iopscience.iop.org
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 …

[HTML][HTML] An improved VMD–EEMD–LSTM time series hybrid prediction model for sea surface height derived from satellite altimetry data

H Chen, T Lu, J Huang, X He, X Sun - Journal of Marine Science and …, 2023‏ - mdpi.com
Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics,
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 …

Prediction of solar cycle 25 using deep learning based long short-term memory forecasting technique

A Prasad, S Roy, A Sarkar, SC Panja… - Advances in Space …, 2022‏ - Elsevier
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 …

ECG-based heartbeat classification using exponential-political optimizer trained deep learning for arrhythmia detection

A Choudhury, S Vuppu, SP Singh, M Kumar… - … Signal Processing and …, 2023‏ - Elsevier
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 …

Electricity price forecast based on the STL-TCN-NBEATS model

B Zhang, C Song, X Jiang, Y Li - Heliyon, 2023‏ - cell.com
Taking long-term high-frequency electricity price data as the research content, this paper
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 …