These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
120 related articles for article (PubMed ID: 36070317)
1. Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy. Butt UM; Letchmunan S; Hassan FH; Koh TW PLoS One; 2022; 17(9):e0274172. PubMed ID: 36070317 [TBL] [Abstract][Full Text] [Related]
2. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Ayoobi N; Sharifrazi D; Alizadehsani R; Shoeibi A; Gorriz JM; Moosaei H; Khosravi A; Nahavandi S; Gholamzadeh Chofreh A; Goni FA; Klemeš JJ; Mosavi A Results Phys; 2021 Aug; 27():104495. PubMed ID: 34221854 [TBL] [Abstract][Full Text] [Related]
3. Leveraging transfer learning with deep learning for crime prediction. Butt UM; Letchmunan S; Hassan FH; Koh TW PLoS One; 2024; 19(4):e0296486. PubMed ID: 38630687 [TBL] [Abstract][Full Text] [Related]
4. 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]
5. Applying SARIMA, ETS, and hybrid models for prediction of tuberculosis incidence rate in Taiwan. Kuan MM PeerJ; 2022; 10():e13117. PubMed ID: 36164599 [TBL] [Abstract][Full Text] [Related]
6. A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study. Yu CS; Chang SS; Chang TH; Wu JL; Lin YJ; Chien HF; Chen RJ J Med Internet Res; 2021 May; 23(5):e27806. PubMed ID: 33900932 [TBL] [Abstract][Full Text] [Related]
7. Analysis and forecasting of syphilis trends in mainland China based on hybrid time series models. Wang ZD; Yang CX; Zhang SK; Wang YB; Xu Z; Feng ZJ Epidemiol Infect; 2024 May; 152():e93. PubMed ID: 38800855 [TBL] [Abstract][Full Text] [Related]
8. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Shahid F; Zameer A; Muneeb M Chaos Solitons Fractals; 2020 Nov; 140():110212. PubMed ID: 32839642 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. The improved integrated Exponential Smoothing based CNN-LSTM algorithm to forecast the day ahead electricity price. Shejul K; Harikrishnan R; Gupta H MethodsX; 2024 Dec; 13():102923. PubMed ID: 39263362 [TBL] [Abstract][Full Text] [Related]
11. Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model. Li G; Li Y; Han G; Jiang C; Geng M; Guo N; Wu W; Liu S; Xing Z; Han X; Li Q BMC Public Health; 2024 Aug; 24(1):2171. PubMed ID: 39135162 [TBL] [Abstract][Full Text] [Related]
12. Studies on predicting soil moisture levels at Andhra Loyola College, India, using SARIMA and LSTM models. Kumar MT; Rao MC Environ Monit Assess; 2023 Nov; 195(12):1426. PubMed ID: 37935939 [TBL] [Abstract][Full Text] [Related]
13. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models. Adeyinka DA; Muhajarine N BMC Med Res Methodol; 2020 Dec; 20(1):292. PubMed ID: 33267817 [TBL] [Abstract][Full Text] [Related]
14. 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]
15. Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China. Zhao Z; Zhai M; Li G; Gao X; Song W; Wang X; Ren H; Cui Y; Qiao Y; Ren J; Chen L; Qiu L BMC Infect Dis; 2023 Feb; 23(1):71. PubMed ID: 36747126 [TBL] [Abstract][Full Text] [Related]
16. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study. Wang YW; Shen ZZ; Jiang Y BMJ Open; 2019 Jun; 9(6):e025773. PubMed ID: 31209084 [TBL] [Abstract][Full Text] [Related]
17. A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020. Zenia S; L'Hadj M; Selmane S J Res Health Sci; 2023 Sep; 23(3):e00586. PubMed ID: 38315901 [TBL] [Abstract][Full Text] [Related]
18. A new hybrid model SARIMA-ETS-SVR for seasonal influenza incidence prediction in mainland China. Zhao D; Zhang R J Infect Dev Ctries; 2023 Nov; 17(11):1581-1590. PubMed ID: 38064398 [TBL] [Abstract][Full Text] [Related]
19. Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province. Yang W; Su A; Ding L BMC Public Health; 2023 Nov; 23(1):2309. PubMed ID: 37993836 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]