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<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="healthcare" lang="en"><front><journal-meta><journal-id journal-id-type="publisher">IJCRR</journal-id><journal-id journal-id-type="nlm-ta">I Journ Cur Res Re</journal-id><journal-title-group><journal-title>International Journal of Current Research and Review</journal-title><abbrev-journal-title abbrev-type="pubmed">I Journ Cur Res Re</abbrev-journal-title></journal-title-group><issn pub-type="ppub">2231-2196</issn><issn pub-type="opub">0975-5241</issn><publisher><publisher-name>Radiance Research Academy</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">4423</article-id><article-id pub-id-type="doi"/><article-id pub-id-type="doi-url">http://dx.doi.org/10.31782/IJCRR.2022.14705</article-id><article-categories><subj-group subj-group-type="heading"><subject>Healthcare</subject></subj-group></article-categories><title-group><article-title>Smart SOP__ampersandsignrsquo;s Surveillance System using Deep Neural Network&#13;
</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Lal</surname><given-names>Chaman</given-names></name></contrib><contrib contrib-type="author"><name><surname>Ali</surname><given-names>Zahid</given-names></name></contrib><contrib contrib-type="author"><name><surname>Aftab</surname><given-names>Shagufta</given-names></name></contrib><contrib contrib-type="author"><name><surname>Beejal</surname><given-names>Suresh Kumar</given-names></name></contrib><contrib contrib-type="author"><name><surname>Shaikh</surname><given-names>Mehwish</given-names></name></contrib><contrib contrib-type="author"><name><surname>Fatima</surname><given-names>Ambreen</given-names></name></contrib></contrib-group><pub-date pub-type="ppub"><day>5</day><month>04</month><year>2022</year></pub-date><volume>)</volume><issue/><fpage>6</fpage><lpage>11</lpage><permissions><copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement><copyright-year>2009</copyright-year><license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p></license></permissions><abstract><p>The global pandemic Corona virus sickness 2019 had a devastating impact on the world, according to statistics received by the World Health Organization. A study shows that about 33 million people are affected by the virus and more than 1 million deaths are reported worldwide. Currently, there are no specific vaccines or treatments for COVID-19. Now, WHO recommends that wearing a mask and following the SOP__ampersandsignrsquo;s can help us in preventing the spread of the virus. There are only few studies about face mask detection based on image analysis. To create a safe environment in public places, educational institutions, __ampersandsignamp; shopping malls we are proposed an effective idea based on computer vision and AI which is focused on real-time automated monitoring through the camera to detect people whether they are wearing a mask or not, also checks body temperature by the sensor-based model. In this proposed system modern deep learning algorithms have been mixed with different techniques to detect the unmasked faces and temperature and generate warning alerts by capturing images of the people not wearing masks and if the person has temperature, then it will also generate an alert of having temperature. This system favors society in preventing the spread of the virus as it has already infected 33 million people worldwide. The goal is to evaluate the face detection through image processing to prevent society from the spread of global pandemic disease and pollution.&#13;
</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd> SOP’s</kwd><kwd> CNN</kwd><kwd> Computer vision</kwd><kwd> DNN</kwd><kwd/></kwd-group></article-meta></front></article>
