DCU-Final-Year-Projects-Booklet-2025

122 270. TimelineXtract This project developed amachine learning application to streamline the management of clinical trial protocols, focusing on both patient-reported and clinician-reported outcomes. It automated the extraction of key protocol details—such as titles, patient numbers, trial sites, therapeutic areas, and associated questionnaires and their schedules—using NLP techniques and a small-scale knowledge graph. Designed for pharmaceutical and healthcare organisations, the system reducedmanual effort, improved accuracy, and accelerated clinical trial execution. Publicly available data fromClinicalTrials.gov was used to train and validate the models. The project utilised tools like Adobe API, GPT-4, and Neo4j to demonstrate howAI and graph databases can enhance clinical trial efficiency and data organisation. Student Programme Computer Science Project Area Artificial Intelligence, Automation, Biomedical Engineering, DataMining, Databases, Natural Language Processing, Software Development Project Technology HTML5, JavaScript, MongoDB, Python, React.js, Machine Learning, Bioprocessing, Adobe API, Natural Language Processing, Neo4j Student Name(s) Lorena Gomez  |  DarraghManning Email lorena.gomez3@mail.dcu.ie  |  darragh.manning8@mail.dcu.ie Supervisor Prof TomasWard 271. Clockwork DrivenMicrofluidic Pump Microfluidic pumps are essential for transporting tiny amounts of fluid through small channels, enabling applications in medical diagnostics and drug delivery. Traditional microfluidic pumps often require electrical power, limiting their use in remote or resource-limited settings. This project focuses on the design andmanufacture of a low-energy microfluidic peristaltic pump powered by a clockwork mechanism. By harnessing the mechanical energy stored in a wound spring, the systemprovides a reliable and portable alternative to conventional pumps. The clockwork-driven motion ensures precise fluid control, making it ideal for point-of-care medical diagnostics. Student Programme Mechanical andManufacturing Engineering (Year 4) Project Area 3-DModelling, Biomedical Engineering, Device Design, FluidMechanics, Mechanical Design andManufacture, Renewable Energy Technology, Wearable_Technology Project Technology Solidworks, microfluidics Student Name(s) MalachyWoodcock Email malachy.woodcock3@mail.dcu.ie Supervisor Dr David Kinahan 272. ClearLens – UncoveringMedia Bias for aMore InformedNews Experience In today’s fast-pacedmedia environment where subtle biases can shape opinions without readers realising it, ClearLens provides a platformwhere users know the political stance of an article before they have even read the first sentence. ClearLens is designed with themodern news consumer inmind, delivering quick, accessible insights that fit seamlessly into the rapid way people consume content today. Our goal is to empower users tomake informed decisions about the media they consume and encourage balanced perspectives across a range of topics. The combination of accurate bias detection with an engaging interface, allows ClearLens to transform the consumption of media and foster amore objective user experience. Student Programme Computer Science Project Area Artificial Intelligence, Data Analytics, Databases, Educational, Natural Language Processing, Software Development, Web Application Project Technology AngularJS, CSS, Docker, HTML5, JavaScript, Python, REST, SQL, Machine Learning Student Name(s) Zoe Collins  |  SofiaMargarita Cuesta Barrett Email zoe.collins2@mail.dcu.ie  |  sofia.cuestabarrett2@mail.dcu.ie Supervisor Dr AlessandraMileo