SMU Data Science Review
The SMU Data Science Review is a repository of SMU Masters student work in the field of data science as well as an electronic journal that promotes data-driven scientific discovery and welcomes experimental and theoretical research on advanced data science technologies and their real world applications.
See the Aims and Scope for a complete coverage of the journal.
Current Issue: Volume 8, Number 1 (2024) Spring 2024
Articles
Leveraging Transformer Models for Genre Classification
Andreea C. Craus, Ben Berger, Yves Hughes, and Hayley Horn
Personalization: University Fundraising
Jasmine O'Neal, Akib Hossain, and Yogesh Bhalerao
Game Recommendation Analysis Using Steam Profiles and Reviews
Robert Blue, Luis Garcia, and Jacob Turner
Enhancing Customer Support Operations through GPT & Q-Learning: A Model Study
Adam Alidra, Bob O'Brien, and Dalton Young
Building Effective Large Language Model Agents
Sydney Holder and Shreyash Taywade
Multi-Class Emotion Classification with XGBoost Model Using Wearable EEG Headband Data
James Khamthung, Nibhrat Lohia, and Seement Srivastava
Reevaluating Texas Energy Market Forecasts in The Wake of Recent Extreme Weather Events
Robert A. Derner, Richard W. Butler II, Alexandria Neff, and Adam R. Ruthford
Baseball Decision-Making: Optimizing At-bat Simulations
Varun Gopal, Krithika Kondakindi, Nibhrat Lohia, and Morgan Williams
Intelligent Solutions for Retroactive Anomaly Detection and Resolution with Log File Systems
Derek G. Rogers, Chanvo Nguyen, and Abhay Sharma
A Symbolic Approach to Nonlinear Time Series Analysis
Ranjan Karki, Nibhrat Lohia, and Michael B. Schulte
Predictive Analysis of Local House Prices: Leveraging Machine Learning for Real Estate Valuation
Joey Hernandez, Danny Chang, Santiago Gutierrez, and Paul Huggins
