Kallel, A., Rekik, M., & Khemakhem, M. (2022). Hybrid-based framework for COVID-19 prediction via federated machine learning models. Journal of Supercomputing, 78(5), 7078-7105. https://doi.org/10.1007/s11227-021-04166-9
ISO-690 (author-date, English)KALLEL, Ameni, REKIK, Molka und KHEMAKHEM, Mahdi, 2022. Hybrid-based framework for COVID-19 prediction via federated machine learning models. Journal of Supercomputing. 1 April 2022. Vol. 78, no. 5, p. 7078-7105. DOI 10.1007/s11227-021-04166-9.
Modern Language Association 9th editionKallel, A., M. Rekik, und M. Khemakhem. „Hybrid-Based Framework for COVID-19 Prediction via Federated Machine Learning Models.“. Journal of Supercomputing, Bd. 78, Nr. 5, April 2022, S. 7078-05, https://doi.org/10.1007/s11227-021-04166-9.
Mohr Siebeck - Recht (Deutsch - Österreich)Kallel, Ameni/Rekik, Molka/Khemakhem, Mahdi: Hybrid-based framework for COVID-19 prediction via federated machine learning models., Journal of Supercomputing 2022, 7078-7105.
Emerald - HarvardKallel, A., Rekik, M. und Khemakhem, M. (2022), „Hybrid-based framework for COVID-19 prediction via federated machine learning models.“, Journal of Supercomputing, Vol. 78 No. 5, S. 7078-7105.