• Vinay Balhara
• Parveen Kantha

Researchers at COVID-19 are focusing on machine learning as a means of improving methods for identifying face masks. In the field of machine learning, there are presently a number of dedicated machines. Despite this, there is a pressing need to enhance machine learning's accuracy and efficiency. In order to limit the transmission of Coronavirus, this study presents a deep learning-based technique for identifying disguised identities in public settings. An ensemble of single and two-stage detectors is used in the proposed method to efficiently handle various occlusions in congested environments. Using the ensemble method, the detection procedure should be much faster without losing accuracy. The study uses earlier research on the Internet of Things, pattern rearrangement, and face mask identification to identify concerns that need to be addressed in the future. Compression, edge detection, and CNN are combined in a unique way in this study, which has the potential to both enhance performance and accuracy. Error rates and performance are also being considered in the research.

CNN, Machine Learning, Face mask Detection, Covid-19, IoT, Error rate, Performance.

"PERFORMANCE ENHANCEMENT AND ERROR RATE REDUCTION IN COVID-19 PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.b382-b389, August-2022, Available :http://www.jetir.org/papers/JETIR2208151.pdf

"PERFORMANCE ENHANCEMENT AND ERROR RATE REDUCTION IN COVID-19 PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppb382-b389, August-2022, Available at : http://www.jetir.org/papers/JETIR2208151.pdf