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 7, Number 1 (2023)
Articles
Extending the M3-Competition: Category and Interval-Specific Time Series Forecasting
Will Sherman, Kati Schuerger, Randy Kim, and Bivin Sadler
Professor Text: University Fundraising Optimization
Braden Anderson, Connor Dobbs, Hien Lam, and John Santerre
Content-Based Unsupervised Fake News Detection on Ukraine-Russia War
Yucheol Shin, Yvan Sojdehei, Limin Zheng, and Brad Blanchard
Using NLP to Model U.S. Supreme Court Cases
Katherine Lockard, Robert Slater, and Brandon Sucrese
The Role of Machine Learning in Improved Functionality of Lower Limb Prostheses
Joaquin Dominguez, Richard Kim, and Robert Slater
Profiting from Dow Jones Industrial Index and Hang Seng Index using moving average and MACD optimization model
Anthony Yeung, Joe Wailun Chung, Nibhrat Lohia, and Onyeka Emmanuel
Following the Crowd: Beginners Investors Guide to the Options Market
Jeremy Dawkins, Alexy Morris, Jacob Gipson, and Masoud Valizadeh
Comparison of Sampling Methods for Predicting Wine Quality Based on Physicochemical Properties
Robert Burigo, Scott Frazier, Eli Kravez, and Nibhrat Lohia
Question Answering with distilled BERT models: A case study for Biomedical Data
Brittany Lewandowski, Rayon Morris, Pearly Merin Paul, and Robert Slater
Bridging The Chasm Between Fundamental, Momentum, and Quantitative Investing
Allen Hoskins, Jeff Reed, and Robert Slater
Identifying Features and Predicting Consumer Helpfulness of Product Reviews
Triston Hudgins, Shijo Joseph, Douglas Yip, and Gaston Besanson