{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:18:41Z","timestamp":1781716721425,"version":"3.54.5"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,10,8]],"date-time":"2023-10-08T00:00:00Z","timestamp":1696723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The extensive potential of Internet of Things (IoT) technology has enabled the widespread real-time perception and analysis of health conditions. Furthermore, the integration of IoT in the healthcare industry has resulted in the development of intelligent applications, including smartphone-based healthcare, wellness-aware recommendations and smart medical systems. Building upon these technological advancements, this research puts forth an enhanced framework designed for the real-time monitoring, detection and prediction of health vulnerabilities arising from air pollution. Specifically, a four-layered model is presented to categorize health-impacting particles associated with air pollution into distinct classes based on probabilistic parameters of Health Adversity (HA). Subsequently, the HA parameters are extracted and temporally analyzed using FogBus, a fog computing platform, to identify vulnerabilities in individual health. To facilitate accurate prediction, an assessment of the Air Impact on Health is conducted using a Differential Evolution-Recurrent Neural Network. Moreover, the temporal analysis of health vulnerability employs the Self-Organized Mapping technique for visualization. The proposed model\u2019s validity is evaluated using a challenging dataset comprising nearly 60 212 data instances obtained from the online University of California, Irvine repository. Performance enhancement is assessed by comparing the proposed model with state-of-the-art decision-making techniques, considering statistical parameters such as temporal effectiveness, coefficient of determination, accuracy, specificity, sensitivity, reliability and stability.<\/jats:p>","DOI":"10.1093\/comjnl\/bxad099","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T10:54:00Z","timestamp":1696935240000},"page":"1763-1782","source":"Crossref","is-referenced-by-count":5,"title":["An Intelligent Air Monitoring System For Pollution Prediction: A Predictive Healthcare Perspective"],"prefix":"10.1093","volume":"67","author":[{"given":"Veerawali","family":"Behal","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University , Punjab"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramandeep","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University , Punjab"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"2024062312371895000_ref1","doi-asserted-by":"crossref","first-page":"717","DOI":"10.3390\/su15010717","article-title":"A survey on iot-enabled smart grids: technologies, architectures, applications, and challenges","volume":"15","author":"Kirmani","journal-title":"Sustainability"},{"key":"2024062312371895000_ref2","first-page":"50","article-title":"A comprehensive health assessment framework to facilitate iot-assisted smart workouts: a predictive healthcare perspective","volume":"92","author":"Bhatia","journal-title":"Comput. 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