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Major Research Project Library

The student is required to conduct an applied advanced research project. The project will be carried out under the guidance of a supervisor. On completion of the project, the results are submitted in a technical report format to an examining committee and the student will make an oral presentation of the report to the committee for assessment and grading of the report. The student is expected to provide evidence of competence in the carrying out of a technical project and present a sound understanding of the material associated with the research project.

The major research paper is presented to the university in partial fulfillment of the requirements for the degree of Master of Science in the program of Data Science and Analytics.  

The MRPs listed below are from the most recent graduates. The catalogue of all MRP abstracts from 2017 and 2019 is available PDF filehere.

  • Afsar, Tazin (2019) – Chest X-Ray Segmentation Utilizing Convolutional Neural Network (CNN)
  • Ahmed, Sayed (2019) – Effect of Dietary Patterns on Chronic Kidney Disease (CKD) Measures (ACR), and on the Mortality of CKD Patients
  • Barolia, Imran (2019) – Synonym Detection with Knowledge Bases
  • Boland, Daniel (2019) – Battery Dispatching for Peak Shaving Using Reinforcement Learning Approaches
  • Cai, Yutian (2019) - Musculoskeletal Disorders Detection With Convolutional Neural Network
  • Choi, Claudia (2019) – Using Deep Learning and Satellite Imagery to Predict Road Safety
  • Chowdhury, Mushfique (2019) – Forecasting Sales and Return Products For Retail Corporation and Bridging Among Them
  • Ensafi, Yasaman (2019) – Neural Network Approach For Seasonal Items Forecasting of a Retail Store
  • Etwaroo, Rochelle (2019) - A Non-Factoid Question Answering System for Prior Art Search
  • Hosmani, Chaitra (2019) – User Interest Detection in Social Media Using Dynamic Link Prediction
  • Islam, Samiul (2019) – Product Backorder Prediction Using Machine Learning Techniques to Minimize Revenue Loss With Efficient Inventory Control
  • House-Senapati, Kristie (2019) - The Use of Recommender Systems for Defect Rediscoveries
  • Husna, Asma (2019) - Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods 
  • Lee, Veson (2019) – Estimating Volatility Using A LSTM Hybrid Neural Network
  • Matta, Rafik (2019) - Deep Learning to Enhance Momentum Trading Strategies using LSTM on the Canadian Stock Market
  • Natarajan, Rajaram (2019) - Road Networks – Intersections and Traffic
  • Ozyegen, Ozan (2019) – Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands
  • Patel, Eisha (2019) – Generating Stylistic Images Using Convolutional Neural Networks
  • Peachey Higdon, Ben (2019) – Time-Series-Based Classification of Financial Forecasting Discrepancies
  • Postma, Cassandra (2019) - Netflix Movie Recommendation Using Hybrid Learning Algorithms and Link Prediction
  • Ragbeer, Julien – Peak Tracker (2019) 
  • Raja, Abdur Rehman (2019) – Rating Prediction of Movielens Dataset
  • Roginsky, Sophie (2019) – Radio Coverage Prediction in Urban Environment Using Regression-Based Machine Learning Models
  • Saeed, Usman (2019) - Digital Text Forensic: Bots and Gender Classification on Twitter Data
  • Sokalska, Iwona (2019) - Boosting Bug Localization with Visual Input and Self-Attention
  • Tabassum, Anika (2019) – Developing a Confidence Measure Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks
  • Zhang, Shulin (2019) – Artificial Neural Networks in Modelling the Term Structure of Interest Rates
  • Zhao, Xin (2019) – Station Based Bike Sharing Demand Prediction