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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 from 2020. The catalogue of all MRP abstracts from 2017 to 2020 is available PDF filehere.

  • Abdolali-Senejani, Ali – Investigating the Challenges of Building a Robust Network Intrusion Detection System Through Assessment of Features and Machine Learning Models
  • Ahmed, Sabbir – Identifying White Blood Cell Sub Type from Blood Cell Images Using Deep Learning Algorithms
  • Anumanchineni, Harish – Cardiovascular Risk Detection Using Machine Learning and Artificial Neural Networks
  • Araujo, Gregory – Transport Mode Detection Using Deep Learning Networks
  • Bagheri, Moeen – Multi-Step Forecasting of Walmart Products
  • Beilis Banelis, Aleksander – Loan Outcome Prediction in P2P Lending
  • Chane, Gagandip – Smart Reply for Online Patient-Doctor Chats
  • Chow, Roger – IDC Prediction in Breast Cancer Histopathology Images
  • Chowdhury, Mohsena – Investigating Organizational Crisis Using Text Mining Technique
  • Cohen, Rory – Predicting the Profitability of Canada’s Big Five Banks: Predictive Analysis With Google Trends and Twitter
  • Dhall, Ankit – Household Space Heating Demand Modelling Using Simplified Black-Box Models
  • Emamidoost, Maryam – Application of Deep Learning in the Segmentation of the Brain Regions to Predict Alzheimer’s Disease
  • Ewen, Nicolas – Self Supervision for Classification on Small Medical Imaging Datasets
  • Ghavifekr, Amin – Machine Learning Approach in Forex (Foreign Exchange) Market Forecasting
  • Gupta, Vatsla – Automated Hate Speech Detection Using Deep Learning Models
  • Hemel, Tahseen Amin – A Deep Learning Approach in Detecting Financial Fraud
  • Houshmand, Bita – Facial Expression Recognition Under Partial Occlusion
  • Ilic, Igor – Explainable Boosted Linear Regression
  • Ioi, Kevin – Dirichlet Multinomial Mixture Models for the Automated Annotation of Financial Commentaries
  • Ionno, Anthony – Benchmarking Machine Learning Prediction Methods on an Open Dataset of Hourly Electricity Data for a Set of Non-Residential Buildings
  • Kabe, Devika – Text Highlighting to Improve Quality of Online Medical Services
  • Kamei, Josephine – Predicting the Remaining Useful Life of the C-MAPSS Turbofan Engine Simulation Dataset FD001
  • Karami, Zahra – Cluster Analysis of Stock For Efficient Portfolio Management
  • Lalonde, Rebecca – Direct Marketing Modelling: Comparing Accuracy and True Positive Rates of Classification Models
  • Li, Vivian – Predicting Stock Market Volume Changes with News Article Topics
  • Malik, Garima – Predicting Financial Commentaries Using Deep Neural Networks
  • Milacic, Dejan – Neural Style Transfer of Environmental Audio Spectograms
  • Murad, Mohammad Wahidul Islam – Demand Forecasting For Wholesale Sales by Industry Considering Seasonality Demand
  • Oyetola, Oyindamola – Predicting Housing Prices Using Deep Neural Networks
  • Parker, Megan – Predicting Stages of Dementia: An Exploration of Feature Selection and Ensemble Methods
  • Patel, Kshirabdhi – Insight Extraction from Regulatory Documents Using Text Summarization Techniques
  • Percival, Dougall – Experiments in Human-Interpretable Feature Extraction for Medical Narrative Classification
  • Rezwan, Asif – Analysis of Daily Weather Data in Toronto to Predict Climate Change Using Bayesian Approach
  • Saha, Milan – Consumer Opinion Classification for Major Canadian Telecom Operators
  • Saleem, Muhammad Saeed – Speech Recognition on English and French Dataset
  • Saleem, Waleed – Recognizing Pattern Based Maneuvers of Traffic Accidents in Toronto
  • Seerala, Pranav Kumar – Classification of Chest X-Ray Images of Pneumonia Patients
  • Shanshal, Dalia - Automated Coroner Report Text Classification: Identifying Opiod Related Deaths
  • Silina, Eugenia – Knowing the Targets When Innoculating Against an Infodemic: Classifying COVID-19 Related News Claims
  • Somisetty, Kusumanjali – Online Detection of User’s Anomalous Activities Using Logs
  • Song, Tianci – Damaged Property Detection With Convolutional Neural Networks
  • Thanabalasingam, Mathusan – Using ProtoPNet to Interpret Alzheimer’s Disease Classification over MRI images
  • Tsang, Leo – Predicting NBA Draft Candidates Using College Statistics
  • Uddin, Md Rokon – Demands and Sales Forecasting for Retailers by Analyzing Google Trends and Historical Data
  • Xu, Shaofang – Credit Risk Rating Model Development via Machine Learning
  • Yeasmin, Nilufa – A Prediction Model for Chest Radiology Reports and Capturing Uncertainties of Radiograph Using Convolutional Neural Network
  • Zhang, Dongrui – Predicting Exchange Rate of Currency by LSTM Model