<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/3d0a25ada9424c4f965d5fb95ff01e9c&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/3d0a25ada9424c4f965d5fb95ff01e9c-00001.gif</thumbnail_url><duration>94.86</duration><title>Coral Disease Outbreak Detection</title><description>Hey, votes face. I&apos;m a part of Team Coral X. Our goal is to detect coral disease outbreaks. Currently, coral bleaching is detected, but the cause of mass coral death is not identified with AI. In this video, we present a simple AI model that predicts the percent of bleaching based on water clarity. We explain how we obtained and processed the bleaching dataset to train our model. Join us to learn more about our approach and how we can make a difference in detecting coral disease outbreaks.</description></oembed>