{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:38:29Z","timestamp":1769164709212,"version":"3.49.0"},"reference-count":30,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T00:00:00Z","timestamp":1596672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPCC"],"published-print":{"date-parts":[[2020,8,6]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>For data collection, Infrared Thermometer, Hikvision\u2019s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The proposed research collected COVID \u221219 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijpcc-07-2020-0082","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T07:47:26Z","timestamp":1597045646000},"page":"477-487","source":"Crossref","is-referenced-by-count":87,"title":["Pervasive computing in the context of COVID-19 prediction with AI-based algorithms"],"prefix":"10.1108","volume":"16","author":[{"given":"Magesh","family":"S.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niveditha","family":"V.R.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajakumar","family":"P.S.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radha RamMohan","family":"S.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natrayan","family":"L.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020100504360974900_ref001","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.17.20070094","article-title":"Covid-19 outbreak prediction with machine learning","volume-title":"MedRxiv, the Preprint Server for Health Sciences","year":"2020"},{"issue":"3","key":"key2020100504360974900_ref002","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1631\/FITEE.1500415","article-title":"BORON: an ultra-lightweight and low power encryption design for pervasive computing","volume":"18","year":"2017","journal-title":"Frontiers of Information Technology and Electronic Engineering"},{"issue":"4","key":"key2020100504360974900_ref003","first-page":"421","article-title":"A survey of people-centric sensing studies utilizing mobile phone sensors","volume":"9","year":"2017","journal-title":"Journal of Ambient Intell.Smart Envrion"},{"issue":"3","key":"key2020100504360974900_ref004","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/s41579-018-0118-9","article-title":"Origin and evolution of pathogenic coronaviruses","volume":"17","year":"2019","journal-title":"Nature Reviews Microbiology"},{"issue":"21","key":"key2020100504360974900_ref005","doi-asserted-by":"crossref","first-page":"6297","DOI":"10.1007\/s00500-016-2183-1","article-title":"Developing a trust model for pervasive computing based on apriori association rules learning and bayesian classification","volume":"21","year":"2017","journal-title":"Soft Computing"},{"issue":"5","key":"key2020100504360974900_ref006","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MIC.2015.72","article-title":"Multimodal wearable sensing for fine-grained activity recognition in healthcare","volume":"19","year":"2015","journal-title":"IEEE Internet Computing"},{"issue":"11","key":"key2020100504360974900_ref007","first-page":"e247","article-title":"Smartphone for smarter delivery of mental health programs: a systematic review","volume":"15","year":"2014","journal-title":"Journal of Medical Internet Research"},{"key":"key2020100504360974900_ref008","unstructured":"Enjuanes, L., Brian, D., Cavanagh, D., Holmes, K., Lai, M.M.C., Laude, H., Maniloff, J., Mayo, M.A., McGeoch, D.J., Pringle, C.R. and Wickner, R.B. 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