212 related articles for article (PubMed ID: 37024922)
1. Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning.
Lu X; Qiu H
BMC Med Inform Decis Mak; 2023 Apr; 23(1):59. PubMed ID: 37024922
[TBL] [Abstract][Full Text] [Related]
2. Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.
Wang P; Zhang W; Wang H; Shi C; Li Z; Wang D; Luo L; Du Z; Hao Y
BMC Infect Dis; 2024 Feb; 24(1):265. PubMed ID: 38408967
[TBL] [Abstract][Full Text] [Related]
3. Seasonal prediction of daily PM
Wu Y; Lin S; Shi K; Ye Z; Fang Y
Environ Sci Pollut Res Int; 2022 Jun; 29(30):45821-45836. PubMed ID: 35150424
[TBL] [Abstract][Full Text] [Related]
4. Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.
Ye GH; Alim M; Guan P; Huang DS; Zhou BS; Wu W
PLoS One; 2021; 16(3):e0248597. PubMed ID: 33725011
[TBL] [Abstract][Full Text] [Related]
5. A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China.
Li X; Zhang X
Environ Sci Pollut Res Int; 2023 Nov; 30(55):117485-117502. PubMed ID: 37867169
[TBL] [Abstract][Full Text] [Related]
6. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.
Zhang X; Zhang Q; Zhang G; Nie Z; Gui Z; Que H
Int J Environ Res Public Health; 2018 May; 15(5):. PubMed ID: 29883381
[TBL] [Abstract][Full Text] [Related]
7. A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods.
Dostmohammadi M; Pedram MZ; Hoseinzadeh S; Garcia DA
J Environ Manage; 2024 Jul; 364():121264. PubMed ID: 38870783
[TBL] [Abstract][Full Text] [Related]
8. Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation.
Khullar S; Singh N
Environ Sci Pollut Res Int; 2022 Feb; 29(9):12875-12889. PubMed ID: 33988840
[TBL] [Abstract][Full Text] [Related]
9. Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks.
Ma M; Liu C; Wei R; Liang B; Dai J
J Appl Clin Med Phys; 2022 Mar; 23(3):e13558. PubMed ID: 35170838
[TBL] [Abstract][Full Text] [Related]
10. Research on prediction of daily admissions of respiratory diseases with comorbid diabetes in Beijing based on long short-term memory recurrent neural network.
Zhu Q; Zhang M; Hu Y; Xu X; Tao L; Zhang J; Luo Y; Guo X; Liu X
Zhejiang Da Xue Xue Bao Yi Xue Ban; 2022 Feb; 51(1):1-9. PubMed ID: 35576109
[TBL] [Abstract][Full Text] [Related]
11. Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.
Qiu H; Luo L; Su Z; Zhou L; Wang L; Chen Y
BMC Med Inform Decis Mak; 2020 May; 20(1):83. PubMed ID: 32357880
[TBL] [Abstract][Full Text] [Related]
12. Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method.
Le TT; Kim H; Kang H; Kim H
Sensors (Basel); 2022 Feb; 22(3):. PubMed ID: 35161899
[TBL] [Abstract][Full Text] [Related]
13. Ensemble streamflow forecasting based on variational mode decomposition and long short term memory.
Sun X; Zhang H; Wang J; Shi C; Hua D; Li J
Sci Rep; 2022 Jan; 12(1):518. PubMed ID: 35017569
[TBL] [Abstract][Full Text] [Related]
14. Heterogeneous ensemble learning for enhanced crash forecasts - A frequentist and machine learning based stacking framework.
Ahmad N; Wali B; Khattak AJ
J Safety Res; 2023 Feb; 84():418-434. PubMed ID: 36868672
[TBL] [Abstract][Full Text] [Related]
15. Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method.
Huang JC; Tsai YC; Wu PY; Lien YH; Chien CY; Kuo CF; Hung JF; Chen SC; Kuo CH
Comput Methods Programs Biomed; 2020 Oct; 195():105536. PubMed ID: 32485511
[TBL] [Abstract][Full Text] [Related]
16. Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.
Lou HR; Wang X; Gao Y; Zeng Q
BMC Public Health; 2022 Nov; 22(1):2167. PubMed ID: 36434563
[TBL] [Abstract][Full Text] [Related]
17. Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study.
Sudarshan VK; Brabrand M; Range TM; Wiil UK
Comput Biol Med; 2021 Aug; 135():104541. PubMed ID: 34166880
[TBL] [Abstract][Full Text] [Related]
18. Prediction of hepatitis E using machine learning models.
Guo Y; Feng Y; Qu F; Zhang L; Yan B; Lv J
PLoS One; 2020; 15(9):e0237750. PubMed ID: 32941452
[TBL] [Abstract][Full Text] [Related]
19. Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level.
Ehteram M; Ghanbari-Adivi E
Environ Sci Pollut Res Int; 2023 Aug; 30(40):92903-92921. PubMed ID: 37501025
[TBL] [Abstract][Full Text] [Related]
20. A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda.
Coker ES; Amegah AK; Mwebaze E; Ssematimba J; Bainomugisha E
Environ Res; 2021 Aug; 199():111352. PubMed ID: 34043968
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
