Smart Air Pollution Monitoring SystemView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000699</doi_batch_id><timestamp>1714890370</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 3</title></titles><publication_date><month>02</month><year>2024</year></publication_date><journal_volume><volume>25</volume></journal_volume><issue>3</issue></journal_issue><journal_article><titles><title>Smart Air Pollution Monitoring System</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>Air is one of the most basic and important elements for human beings to survive. Clean and Sound air is the key to a good and healthy life. But nowadays in city life it has become the most threatening factor. Pollution of air has become the most concerning and affected issue now for us. As time goes, people are finding it very difficult to cope with the growing levels of air pollution. This proposed model aims to present a smart air pollution monitoring system that calculates and predicts the air quality in real time in the environment. The proposed system measures the concentration of air pollution causing gases in the environment using sensors. The acquired data is processed using Raspberry Pi and the collected information is stored in Firebase. With the help of the training data set, the air quality in forthcoming days can be predicted using the android application and a notification feature is included to indicate very severe pollution levels. The forecasting is done using the machine learning algorithms such as Linear Regression and Random Forest Regression with accuracy of 96.52% and 99.2% respectively.</p> </p></abstract><publication_date><month>01</month><day>05</day><year>2023</year></publication_date><pages><first_page>125</first_page><last_page>129</last_page></pages><publisher_item><identifier id_type="pii">gnest_04396</identifier></publisher_item><doi_data><doi>10.30955/gnj.004396</doi><timestamp>1714890370</timestamp><resource>https://journal.gnest.org/publication/gnest_04396</resource></doi_data></journal_article></journal></body></doi_batch> </div>
