Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14928
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dc.contributor.authorDhanalakshmi, M-
dc.contributor.authorVyshali Rao, K P-
dc.contributor.authorBhuvaneshwari-
dc.contributor.authorGeetha Rani, E-
dc.date.accessioned2024-03-30T10:10:58Z-
dc.date.available2024-03-30T10:10:58Z-
dc.date.issued2023-
dc.identifier.citationpp. 1104-1109en_US
dc.identifier.isbn9.79835E+12-
dc.identifier.urihttps://doi.org/10.1109/ICACRS58579.2023.10404747-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14928-
dc.description.abstractThe increase in the pollution of air has led to the generation of many health risks. In the present-day much study has been going on to analyse the standard of air circulated in atmosphere has incited. The Inter-networking of Things (IoT) is been used vastly in various domains to boost the standard of the life of people. This paper describes the Internet of Things (IoT) integrated with low-cost sensor networking for monitoring the quality of air. The sensor networks helps in accumulating the values of each component of air. Considering the diffculty of air quality prediction, an ETM model has been proposed. Firstly, a single factor ETM model is designed which obtain the prediction value of each air component. Then, an ETM model with multifactor is designed. The ETM model considers important factors like data on the surrounding environment and weather data. The integrating booster integrates the two models. The results are obtained by assembling the prediction from the sub-nodes. © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisher2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedingsen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectBoosteren_US
dc.subjectEnhanced Term Memory (Etm)en_US
dc.subjectInternet Of Things (Iot)en_US
dc.titleAir Quality Predictor to Reduce Health Risks and Global Warmingen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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