DCU-Final-Year-Projects-Booklet-2025

114 246. Bluetooth RSSI Asset Tracking ImprovementWithMachine Learning This project aims to improve the accuracy of indoor asset tracking systems by leveraging Bluetooth RSSI values andmachine learning techniques. Traditional Bluetooth RSSI-based tracking systems often struggle with inaccuracies caused by interference and signal fluctuations. Our systemwill continuously collect RSSI data fromassets equipped with Bluetooth tags, process this data using amachine learning model, and predict asset locations in real time. Student Programme Computer Science Project Area Android, Artificial Intelligence, Data Analytics, DataMining, Databases, Environmental Mapping, GPS/GIS, RaspberryPi, Software Development, Statistical Analysis, Telecommunications, Wireless Technology Project Technology C/C++, CSS, MySQL, Python, R, SQL, Machine Learning Student Name(s) Brian O’Sullivan  |  Eoin Parkinson Email brian.osullivan38@mail.dcu.ie  |  eoin.parkinson2@mail.dcu.ie Supervisor Mr Renaat Verbruggen 247. Razor Razor is a stock analysis application designed to simplify investing for younger generations and underrepresented groups. The application utilises an LSTMmachine learningmodel to predict stock prices and incorporates sentiment analysis to evaluate market sentiment fromfinancial data. Razor also features technical analysis tools, including indicators such as RSI, MACD, and EMA, to provide users with a comprehensive understanding of market trends. With its user-friendly interface, the appmakes complex financial data accessible, empowering novice investors tomake informed decisions. This project combines advancedmachine learning techniques and technical analysis to promote smarter andmore confident investing. Student Programme Computing for Business Project Area Data Analytics, Machine Learning Project Technology CSS, Docker, JavaScript, Python, Machine Learning Student Name(s) Ryan Enright  |  Ben Butterly Email ryan.enright7@mail.dcu.ie  |  ben.butterly2@mail.dcu.ie Supervisor Dr Marija Bezbradica 248. 3DPrinting of a Low-Cost Modular Microfluidic Chip This project develops a low-cost, reusable 3D-printedmicrofluidic chip, improving reliability and accessibility for point-of-care diagnostics in low-resource settings. Student Programme Mechanical andManufacturing Engineering (Year 4) Project Area 3-DModelling, AdditiveManufacturing, Biomedical Engineering, FluidMechanics Project Technology Solidworks Student Name(s) Edward Hamilton Email edward.hamilton5@mail.dcu.ie Supervisor Dr David Kinahan