01606nas a2200241   4500000000100000000000100001008004100002260001200043653003900055653002500094653001800119653002100137653001100158653002200169100002200191700001700213245007400230856007900304300001000383490000600393520095100399022001401350       2023                            d  c03/202310aConvolutional Neural Network (CNN)10aCoronavirus COVID-1910aDeep Learning10aEnsemble Methods10aHealth10aTransfer Learning1 aPradeep Kumar Roy1 aAshish Singh00aCOVID-19 Disease Prediction Using Weighted Ensemble Transfer Learning  uhttps://www.ijimai.org/journal/sites/default/files/2023-02/ijimai8_1_2.pdf  a13-220 v83 aHealth experts use advanced technological equipment to find complex diseases and diagnose them. Medical imaging nowadays is popular for detecting abnormalities in human bodies. This research discusses using the Internet of Medical Things in the COVID-19 crisis perspective. COVID-19 disease created an unforgettable remark on human memory. It is something like never happened before, and people do not expect it in the future. Medical experts are continuously working on getting a solution for this deadly disease. This pandemic warns the healthcare system to find an alternative solution to monitor the infected person remotely. Internet of Medical Things can be helpful in a pandemic scenario. This paper suggested a ensemble transfer learning framework predict COVID-19 infection. The model used the weighted transfer learning concept and predicted the COVID- 19 infected people with an F1-score of 0.997 for the best case on the test dataset.  a1989-1660