{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:38:19Z","timestamp":1771515499533,"version":"3.50.1"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>Artificial Intelligence (AI) supported data analytics is adopted in variety of domains to process the data with a guaranteed accuracy. The application of the AI-schemes, like MachineLearning (ML) and Deep-Learning (DL) are commonly considered when a faster and accurate image examination is necessary. Hence, AI techniques are frequently utilized to\nprocess gray\/RGB images. This research aims to propose a DL-supported segmentation tool\nto examine the Flood Monitoring Image (FMI) data. The developed system encompasses\nthe following phases: (i) image collection and resizing, (ii) image pre-processing utilizing the Butterfly Algorithm (BA) and Otsu\u2019s\/Kapur\u2019s based multi-threshold, (iii) executing\nDL-segmentation to extract the flood region from the selected image, and (iv) comparing\nsegmented area with the binary mask (BM), and calculating the essential image metrics to\nvalidate tool\u2019s efficacy. This study validates the merit of DL-tool on the unprocessed and preprocessed images. The experimental results of this study demonstrate that the VGG-UNet\nyields superior segmentation outcomes, with better mean value of Jaccard-index (&gt;93%),\nDice-coefficient (&gt;95%), and accuracy (&gt;95%) in comparison to other DL-schemes employed in this research<\/jats:p>","DOI":"10.54364\/aaiml.2025.52213","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T10:50:14Z","timestamp":1751367014000},"page":"3755-3767","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Segmentation of Flood Region in Otsu\u2019s\/Kapur\u2019s Threshold Enhanced Images using Deep-Learning Scheme"],"prefix":"10.54364","volume":"05","author":[{"family":"M. Mathumathi","sequence":"first","affiliation":[]},{"family":"A. Rama","sequence":"additional","affiliation":[]}],"member":"32807","published-online":{"date-parts":[[2025]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/577152213.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T10:50:15Z","timestamp":1751367015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/577152213.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2025.52213","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}