Cargando…

Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

Scientific and reasonable forecast model of graduates' employment data can efficaciously embody the complex characteristics of graduates' employment data and embody the nonlinear dynamic interaction of influencing elements of graduates' employment situation. It has a strong and steady...

Descripción completa

Detalles Bibliográficos
Autores principales:	Li, Xing, Yang, Ting
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Hindawi 2021
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490037/ https://www.ncbi.nlm.nih.gov/pubmed/34616445 http://dx.doi.org/10.1155/2021/5787355

Ejemplares similares

Retracted: Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network
por: Intelligence and Neuroscience, Computational
Publicado: (2023)

A bilateral employment situation prediction model for college students using GCN and LSTM
por: Shen, Junxia
Publicado: (2023)

Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network
por: Zhao, Fang, et al.
Publicado: (2021)

LSTM-based recurrent neural network provides effective short term flu forecasting
por: Amendolara, Alfred B., et al.
Publicado: (2023)

Forecasting the spread of COVID-19 using LSTM network
por: Kumar, Shiu, et al.
Publicado: (2021)

Current college graduates’ employability factors based on university graduates in Shaanxi Province, China
por: Jiang, Lijuan, et al.
Publicado: (2023)

A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks
por: Xu, Shenyi, et al.
Publicado: (2022)

Research on Network Security Situation Awareness Based on the LSTM-DT Model
por: Zhang, Haofang, et al.
Publicado: (2021)

The Employability and Career Development of Finance and Trade College Graduates
por: Huang, Xiang, et al.
Publicado: (2022)

Forecasting of China’s solar PV industry installed capacity and analyzing of employment effect: based on GRA-BiLSTM model
por: Liu, Bingchun, et al.
Publicado: (2021)

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network
por: Li, Zan, et al.
Publicado: (2022)

Time series forecasting for tuberculosis incidence employing neural network models
por: Orjuela-Cañón, Alvaro David, et al.
Publicado: (2022)

A deep LSTM network for the Spanish electricity consumption forecasting
por: Torres, J. F., et al.
Publicado: (2022)

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks
por: Awan, Faraz Malik, et al.
Publicado: (2020)

Application of Improved LSTM Algorithm in Macroeconomic Forecasting
por: Chen, Shijun, et al.
Publicado: (2021)

LSTM-based sentiment analysis for stock price forecast
por: Ko, Ching-Ru, et al.
Publicado: (2021)

Transfer Learning for COVID-19 cases and deaths forecast using LSTM network
por: Gautam, Yogesh
Publicado: (2022)

Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model
por: Shao, Lei, et al.
Publicado: (2022)

Time series forecasting of COVID-19 transmission in Canada using LSTM networks()
por: Chimmula, Vinay Kumar Reddy, et al.
Publicado: (2020)

Neutron transport calculation for the BEAVRS core based on the LSTM neural network
por: Ren, Changan, et al.
Publicado: (2023)

Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting
por: Meng, Xiangyi, et al.
Publicado: (2021)

Forecasting Nonlinear Systems with LSTM: Analysis and Comparison with EKF
por: Llerena Caña, Juan Pedro, et al.
Publicado: (2021)

Forecasting Hazard Level of Air Pollutants Using LSTM’s
por: Gul, Saba, et al.
Publicado: (2020)

Wavelet LSTM for Fault Forecasting in Electrical Power Grids
por: Branco, Nathielle Waldrigues, et al.
Publicado: (2022)

Enhancing efficiency and capacity of telehealth services with intelligent triage: a bidirectional LSTM neural network model employing character embedding
por: Shi, Jinming, et al.
Publicado: (2023)

Scene Text Recognition Based on Bidirectional LSTM and Deep Neural Network
por: Kantipudi, MVV Prasad, et al.
Publicado: (2021)

Application of PSO-based LSTM Neural Network for Outpatient Volume Prediction
por: Lu, Wenjing, et al.
Publicado: (2021)

Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe
por: Ibrahim, Mohamed R., et al.
Publicado: (2021)

A Solar Irradiance Forecasting Framework Based on the CEE-WGAN-LSTM Model
por: Li, Qianqian, et al.
Publicado: (2023)

The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases
por: Absar, Nurul, et al.
Publicado: (2021)

Retracted: Application of Improved LSTM Algorithm in Macroeconomic Forecasting
por: Intelligence and Neuroscience, Computational
Publicado: (2023)

A LSTM-Hawkes hybrid model for posterior click distribution forecast in the advertising network environment
por: Hwang, Sangwon, et al.
Publicado: (2020)

Forecasting shipping index using CEEMD-PSO-BiLSTM model
por: Li, Chengang, et al.
Publicado: (2023)

Forecasting Covid-19 Transmission with ARIMA and LSTM Techniques in Morocco
por: Rguibi, Mohamed Amine, et al.
Publicado: (2022)

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting
por: Aung, Nway Nway, et al.
Publicado: (2023)

Impact of the COVID-19 epidemic anxiety on college students' employment confidence and employment situation perception in China
por: Zheng, Sining, et al.
Publicado: (2022)

Application of LSTM Neural Network Technology Embedded in English Intelligent Translation
por: Yang, Yifang
Publicado: (2022)

Prediction of SO(2) Concentration Based on AR-LSTM Neural Network
por: Ju, Jie, et al.
Publicado: (2022)

Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting
por: Fang, Zheng, et al.
Publicado: (2021)

An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model
por: Lv, Jiehua, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_dsgg5nkn391g4ks3rtnneij4r5