184 related articles for article (PubMed ID: 36406332)
1. Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases.
Qureshi M; Daniyal M; Tawiah K
J Healthc Eng; 2022; 2022():4864920. PubMed ID: 36406332
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.
Daniyal M; Tawiah K; Muhammadullah S; Opoku-Ameyaw K
J Healthc Eng; 2022; 2022():4802743. PubMed ID: 35747601
[TBL] [Abstract][Full Text] [Related]
4. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).
ArunKumar KE; Kalaga DV; Sai Kumar CM; Chilkoor G; Kawaji M; Brenza TM
Appl Soft Comput; 2021 May; 103():107161. PubMed ID: 33584158
[TBL] [Abstract][Full Text] [Related]
5. Prediction of COVID-19 Data Using Hybrid Modeling Approaches.
Zhao W; Sun Y; Li Y; Guan W
Front Public Health; 2022; 10():923978. PubMed ID: 35937245
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Forecasting COVID-19 in Pakistan.
Ali M; Khan DM; Aamir M; Khalil U; Khan Z
PLoS One; 2020; 15(11):e0242762. PubMed ID: 33253248
[TBL] [Abstract][Full Text] [Related]
9. Modeling and Forecasting Monkeypox Cases Using Stochastic Models.
Qureshi M; Khan S; Bantan RAR; Daniyal M; Elgarhy M; Marzo RR; Lin Y
J Clin Med; 2022 Nov; 11(21):. PubMed ID: 36362783
[TBL] [Abstract][Full Text] [Related]
10. Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.
Ghafouri-Fard S; Mohammad-Rahimi H; Motie P; Minabi MAS; Taheri M; Nateghinia S
Heliyon; 2021 Oct; 7(10):e08143. PubMed ID: 34660935
[TBL] [Abstract][Full Text] [Related]
11. Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States.
Makinde OS; Adeola AM; Abiodun GJ; Olusola-Makinde OO; Alejandro A
J Epidemiol Glob Health; 2021 Jun; 11(2):200-207. PubMed ID: 33876598
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models.
Wang Y; Yan Z; Wang D; Yang M; Li Z; Gong X; Wu D; Zhai L; Zhang W; Wang Y
BMC Infect Dis; 2022 May; 22(1):495. PubMed ID: 35614387
[TBL] [Abstract][Full Text] [Related]
14. Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.
Zhang R; Song H; Chen Q; Wang Y; Wang S; Li Y
PLoS One; 2022; 17(1):e0262009. PubMed ID: 35030203
[TBL] [Abstract][Full Text] [Related]
15. Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India.
Swaraj A; Verma K; Kaur A; Singh G; Kumar A; Melo de Sales L
J Biomed Inform; 2021 Sep; 121():103887. PubMed ID: 34407487
[TBL] [Abstract][Full Text] [Related]
16. Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.
Zhao D; Zhang R; Zhang H; He S
Sci Rep; 2022 Oct; 12(1):18138. PubMed ID: 36307471
[TBL] [Abstract][Full Text] [Related]
17. ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021.
Wang M; Pan J; Li X; Li M; Liu Z; Zhao Q; Luo L; Chen H; Chen S; Jiang F; Zhang L; Wang W; Wang Y
BMC Public Health; 2022 Jul; 22(1):1447. PubMed ID: 35906580
[TBL] [Abstract][Full Text] [Related]
18. [A comparative study of time series models in predicting COVID-19 cases].
Li ZQ; Tao B; Zhan M; Wu Z; Wu J; Wang J
Zhonghua Liu Xing Bing Xue Za Zhi; 2021 Mar; 42(3):421-426. PubMed ID: 34814408
[No Abstract] [Full Text] [Related]
19. 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]
20. [Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].
Ke-Wei W; Yu W; Jin-Ping L; Yu-Yu J
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi; 2016 Jul; 28(6):630-634. PubMed ID: 29469251
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
