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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 2024.

The catalogue of all MRP abstracts from 2017 to 2024.  

  • Abdalla, Mazin – Examining the Impact of Listening to Music on Mental Health: An Observational Study
  • Adedigba, Aminat – Detecting Credit Card Fraud Using Exploratory Data Analysis and Machine Learning Techniques
  • Agbolabori, Damilola – Detecting Money Laundering Using Machine Learning Techniques
  • Al-Kaf, Abobakr – Development and Evaluation of an AI-Powered Medical Chatbot for Patient Education  
  • Annadurai, Adisvara – Network Resilience and Community Analysis   
  • Atodaria, Vaidehi – Insights Into India-Canada Relations: Analyzing Public Discourse on Facebook
  • Bisla, Sonal – Sentiment Analysis of Products Review for E-Commerce Platform 
  • Brar, Pavleen – Detection of Misinformation on Social Media Using Deep Learning 
  • Chauhan, Kartikey – LLM-Augmented Knowledge-Graph-Based Recommendation System
  • Duong, Jonathan – Enhancing Flight Delay Prediction Using Machine Learning 
  • Durbha, Nitya – Predictive Maintenance Modelling for Manufacturing Purposes 
  • Ghosh, Pretom – Synthetic Data Generation and Integration of Sentiment Analysis for Time Series Prediction
  • Ghotra, Sarbpreet – Public Sentiment Analysis on Generative AI Through Machine Learning 
  • Gullett, William – Benchmarking Compact Convolutional Transformers Using the BigEarthNet Dataset
  • Gurnani, Kunal – A Hybrid Transformer-Mamba Approach for Landcover Aerial Imagery Segregation 
  • Hassan, Kehinde – Enhancing User Experience in ChatGPT: Sentiment Analysis of Twitter Data for Insights and Improvement 
  • Hassan, Marium – Graph Convolutional Neural Networks for Personalized Medication Recommendations: An Exploration in The Minimization of Drug-Drug Interactions
  • Helyar, Simon – Improving Ontario Air Quality With Machine Learning Models and Mathematical Optimization
  • Islam, Ishraqul – Enhancing Fake Review Detection with Unsupervised GNN
  • Issac, Roshan – Uniq-Bot: An Advanced LLM-Powered Intelligent Conversational Assistant for Swift and Accurate Frequently Answered Questions (FAQs) Assistance in University 
  • Jain, Daksh Rakeshkumar – Portfolio Recommender Based on Individuals Financial Risk Tolerance Level 
  • Janvier, Kurawige - Benchmarking the Performance of Multilingual Transformer Models: A Comparative Study of Swahili and English Corpus on Classification Tasks
  • Joshi, Bhavikummar – Credit Card Fraud Detection Using Advanced Machine Learning Techniques
  • Kaur, Amarpreet – Deepfake Detection
  • Kaur, Ramanjeet – Data Driven Sales Forecasting on Mini Sales Courses 
  • Ladha, Sameer – True North, Strong and Free? Exploring Discourse on Immigrants and Refugees on Canadian Focused Subreddits 
  • Laframboise, Adam – Leveraging Data-Driven Strategies to Mitigate Flight Delays and Cancellations in the Global Aviation Industry 
  • Noor, Nabila – Deepfake Voice Detection: Advanced Machine/Deep Learning Techniques with the DEEP-VOICE Dataset 
  • Onungwe, Chinwendu – Forecasting U.S. Outbound Travel Demand with Hybrid Machine Learning Models 
  • Onungwe, Joshua – Advanced Machine Learning Techniques for Telematics-Based Insurance Risk Assessment
  • Ozyegen, Lara – Transformer-Based Text Highlighting for Medical Terms 
  • Parmar, Ruchi – Optimal Biopsy Decision Making in Breast Cancer Using Reinforcement Learning 
  • Salou Doudou, Nadia – Assessing Recommender System Architectures: A Comparative Study of Collaborative Filtering, Graph-Based and Hypergraph-Based Models 
  • Senthil Kumar, Naren Adithan – Optimizing Urban Mobility Using LSTM and GRU 
  • Shingadia, Tara – Predictive Analytics for Risk Assessment: Machine Learning Approaches to Automobile Loan Default Prediction 
  • Singh, Rupinder – Predicting US Flight Cancellations Weather Data
  • Taher, Murad – Predicting House Prices: Developing an Optimized Model Through a Comprehensive Study of Machine Learning Techniques and Optimisations 
  • Tajdini, Fateme – Integrating Clinical And Demographic Variables for Liver Disease Prediction 
  • Tiwari, Ankita – Road Traffic Accident Prone Analysis
  • Tortola, Anthony – Machine Learning Models for Predicting Emergency Response Times 
  • Umer, Muhammad – Research Proposal on Analyzing and Predicting Cryptocurrency Metrics Using Machine Learning Techniques 
  • Shivani, Vashi – A Comparative Study: Supervised Vs. Unsupervised Music Recommendation System
  • Wang, Xue Qi – Classifying Disaster Tweets Using Natural Language Processing and Machine Learning Techniques 
  • Yu, Hao Yang – Towards Generalizable Hate Speech Detectors: An Analysis of Model Bias and Ensemble Methods