Mask Detection Using IoT - A Comparative Study of Various Learning Models
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13287 LNCS, Page: 272-283
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures4
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Conference Paper Description
Wearing a mask is an effective measure that prevents the spread of respiratory droplets into the air and thereby curtails the dissemination of coronavirus. Unfortunately, despite the proven effectiveness, the idea of wearing a face mask has difficulty being accepted by part of the population. To address this significant health concern, we present a monitoring system that automatically detects whether a mask is put appropriately over a face. The system annotates the videos that are provided by cameras. In this article, we present a comparative study of machine learning models (i.e., SVM, RNN, LSTM, CNN, auto-encoder, MobileNetV2, Net-B3, VGG-16, VGG-19, Resnet-152).
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85134162027&origin=inward; http://dx.doi.org/10.1007/978-3-031-09593-1_23; https://link.springer.com/10.1007/978-3-031-09593-1_23; https://dx.doi.org/10.1007/978-3-031-09593-1_23; https://link.springer.com/chapter/10.1007/978-3-031-09593-1_23
Springer Science and Business Media LLC
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