Real-Time Learning and Monitoring System in Fighting Against Sars-Cov in a Private Indoor Environment

dc.authorid Erişen, Serdar/0000-0002-7192-0889
dc.authorscopusid 57218221719
dc.authorwosid Erişen, Serdar/B-3030-2017
dc.contributor.author Erisen, Serdar
dc.contributor.other Architecture
dc.date.accessioned 2024-07-05T15:24:55Z
dc.date.available 2024-07-05T15:24:55Z
dc.date.issued 2022
dc.department Atılım University en_US
dc.department-temp [Erisen, Serdar] Atilim Univ, Dept Architecture, TR-06830 Ankara, Turkey en_US
dc.description Erişen, Serdar/0000-0002-7192-0889 en_US
dc.description.abstract The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services. en_US
dc.identifier.citationcount 4
dc.identifier.doi 10.3390/s22187001
dc.identifier.issn 1424-8220
dc.identifier.issue 18 en_US
dc.identifier.pmid 36146346
dc.identifier.scopus 2-s2.0-85138331319
dc.identifier.uri https://doi.org/10.3390/s22187001
dc.identifier.uri https://hdl.handle.net/20.500.14411/2477
dc.identifier.volume 22 en_US
dc.identifier.wos WOS:000857650200001
dc.identifier.wosquality Q2
dc.institutionauthor Erişen, Serdar
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 7
dc.subject SARS-CoV-2 en_US
dc.subject real-time learning and monitoring en_US
dc.subject big data en_US
dc.subject indoor air en_US
dc.subject user activity en_US
dc.subject infection transmission control en_US
dc.title Real-Time Learning and Monitoring System in Fighting Against Sars-Cov in a Private Indoor Environment en_US
dc.type Article en_US
dc.wos.citedbyCount 4
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery 79857cf9-13bf-42fb-932a-31d21cb167c6
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