DCU-Final-Year-Projects-Booklet-2025

46 42. Examining the Factors Influencing Injuries in the NFL This project investigates factors contributing to injuries in America’s National (American) Football League (NFL) developing a predictive model usingmachine learning. By analysing play-by-play game data, player statistics, and environmental conditions, the project identifies key variables such as game time, player roles, and field conditions that influence injury occurrence. The predictive model aims at uncovering what are the most influential factors causing player injuries. The analysis includes thousands of plays and leverages advanced statistical andmachine learning techniques to reveal patterns and correlations. This project bridges data science and sports safety, offering valuable contributions to player welfare and injury prevention in professional football. Student Programme Data Science Project Area Artificial Intelligence, Data Analytics, Statistical Analysis, Machine Learning, Sports Analytics Project Technology JavaScript, Python, Machine Learning Student Name(s) Eoin Quinn  |  Ronan Kelly Email eoin.quinn55@mail.dcu.ie   |  ronan.kelly96@mail.dcu.ie Supervisor Prof Mark Roantree 43. TuneTrivia TuneTrivia is an engagingmusic quiz platform that personalises the user experience by integrating with the Spotify API. Users test their music knowledge by guessing the name of a song based on a 30-second snippet of a song. The songs played during each quiz round are based on a custommachine learning algorithm that leverages the user’s Spotify listening history, playlists and other music interests. This ensures that quizzes do not become repetitive, maintaining an engaging experience for the user. This innovative approach aims to create a fun and interactive way to connect with music, making it appealing to diverse audiences. Student Programme Computer Science Project Area Artificial Intelligence, Software Development, Web Application Project Technology JavaScript, MongoDB, Nodejs, React.js, Machine Learning Student Name(s) Torbjorn Hoban  | Madalina Triboi Email torbjorn.hoban3@mail.dcu.ie   | madalina.triboi2@mail.dcu.ie Supervisor Dr Hyowon Lee 44. Incorporating Sustainability KPI’s intoManufacturing Systems of Simulation Studies Manufacturing efficiency is traditionally measured using Overall Equipment Effectiveness (OEE), but it lacks sustainability insights such as energy use, waste, and emissions. This research integrates sustainability Key Performance Indicators (KPIs) into Discrete Event Simulation (DES) using ExtendSim10. A seven-step casting process model is developed to track energy consumption, waste, and carbon emissions, identifying opportunities for improvement. Preliminary results showDES can enhance both efficiency and sustainability. Future work will refine data sources, validate results, and compare baseline vs. sustainable production scenarios, supporting greener manufacturing practices. Student Programme Mechanical and Sustainability Engineering (Year 5) Project Area LeanManufacturing, Mechanical Design andManufacture, Simulation, Statistical Analysis Project Technology ExtendSIM Student Name(s) CianWalsh Email cian.walsh253@mail.dcu.ie Supervisor Dr John Geraghty