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Prediction of Land Use and Landcover Changes in Tiruppur Tamilnadu Using Hybrid Convolutional Neural NetworkView 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>000696</doi_batch_id><timestamp>1714124197</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 6</title></titles><publication_date><month>08</month><year>2024</year></publication_date><journal_volume><volume>25</volume></journal_volume><issue>6</issue></journal_issue><journal_article><titles><title>Prediction of Land Use and Landcover Changes in Tiruppur Tamilnadu Using Hybrid Convolutional Neural Network</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>Land use and landcover change (LU/LC) are important in global change studies because they can transform the local and global environment by developing the biochemical, biochemical and biogeographic properties of the Earth's structure. This paper is intended to develop Hybrid Convolutional Neural Network (HCNN) for land use and land cover changes prediction in Tiruppur Tamilnadu. Initially, the databases are collected from the open-source system. And the image dataset has been pre-processed using the image augmentation technique. Through which the image has been resized and processed for training it with the proposed mode. The resized images are sent to the HCNN for prediction of land cover and land use changes in Tiruppur Tamilnadu. The proposed classifier is a combination of Convolutional Neural Network (CNN) and Remora Optimization Algorithm (ROA). In the CNN, the ROA is utilized to select the hyper parameters to enable efficient prediction in land use and land cover changes. The proposed classifier is implemented in MATLAB and performances is evaluated by performance metrices such as accuracy, precision, recall, sensitivity, F_Measure and Kappa. The proposed methodology is compared with the conventional techniques such as CNN, Markov chain model and Recurrent Neural Network (RNN) respectively.</p> </p></abstract><publication_date><month>03</month><day>22</day><year>2023</year></publication_date><pages><first_page>1</first_page><last_page>11</last_page></pages><publisher_item><identifier id_type="pii">gnest_04716</identifier></publisher_item><doi_data><doi>10.30955/gnj.004716</doi><timestamp>1714124197</timestamp><resource>https://journal.gnest.org/publication/gnest_04716</resource></doi_data></journal_article></journal></body></doi_batch> </div>
