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Trabalho de Conclusão de Curso Identification of Flooding Incident Impacts Using Neural Networks(2024) Santos, Alexandre Magno Maciel dos; Vaz, Eduardo Mendes; Cadorniga, João Lucas de Moraes Barros; Pertusi, Pedro Vaz de MoraesIn a world where flooding impacts are becoming increasingly common, such as the disaster in the Brazilian state of Rio Grande do Sul in 2024, the goal of this project is to develop an open-source flooding impact assessment pipeline. Preliminary technical evaluation by NVIDIA indicates that this tool could be integrated with technologies such as a flood simulation system, allowing for predictions in susceptible regions. The pipeline utilizes Convolutional Neural Networks (CNN), public population, and geographic data to process images extracted from the Sentinel-1 and Sentinel-2 satellites and generate metrics. This project classifies flooded regions prior to and after a crisis, providing, for example, estimations of the affected population by area to showcase the impact to assist urban planning professionals. The developed tool integrates a satellite data collection system from these satellites, as they are also open-source and include periodical data, and the CNNs in an intuitive and easily utilizable pipeline, inspired by UNOSAT’s Emergency Mapping which analyzed the impact of floodings in Nepal in 2021. Keywords:Trabalho de Conclusão de Curso Development of a dashboard for real-time student performance data visualization(2024) Carvalho, Arthur Ferreira; Carreras, Natália Queiroz Menezes; Mahfuz, Pedro OsbornThis project is designed to provide instructors who use the PrairieLearn platform with visual tools and insights into students’ performance by creating and displaying said information in an external dashboard. The data used to create the dashboard is pulled from PrairieLearn’s API. The objective with this is to help instructors model their courses to maximize academic engagement and performance. The PrairieLearn platform offers a dynamic and interactive environment for students to engage with course material, enabling instructors to create customizable quizzes, assignments, and assessments tailored to the evolving educational landscape. With their current system, which provides limited insights by a statistics table that displays average scores and completion times, the project aims to expand the analytical tools available to educators with the creation of this external tool. By integrating features such as performance metric analysis, question score histograms, and assessment completion percentage tracking, the dashboard equips instructors with a detailed view into student performance. This allows for a deeper understanding of assessment effectiveness, enabling educators in identifying learning gaps, adjusting teaching strategies, and customizing content to meet individual student needs more effectively. The anticipated outcome is a user-friendly dashboard, which provides insights into students' learning patterns, allowing instructors to make informed decisions and improve educational outcomes.