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All Journal International Journal of Advanced Science and Computer Applications
françoise Sailhan
Béni Mellal
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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A Comparative Study of Embedded Learning Models IoT-Based For Real time Mask Detection Mohamed Amine Meddaoui; Mohammed Erritali; françoise Sailhan
International Journal of Advanced Science and Computer Applications Vol. 3 No. 2 (2024): September 2024
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v3i2.49

Abstract

Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, face mask detection is a key innovative technology that allows the identification of the number of individuals wearing face masks. In this regard, this paper provides a comparative study of several machine learning and deep learning algorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder, GAN, U-Net GAN) that support mask detection
Co-Authors Mohamed Amine Meddaoui Mohammed Erritali