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 9, Number 2 (2025) Summer 2025
Articles
Analyzing the Global Happiness Index
Victoria Hernandez and Christy W. Wachira
A Comparative Time Series Analysis of the ARIMA and Temporal Fusion Transformer (TFT) Models
Catherine Ticzon, Aaron Abromowitz, and Bivin Sadler
AI-Driven Optimization of Wind Energy Distribution in Texas Using Multi-Agent Reinforcement Learning
Waleed Amer, Owolabi Oluwadamilola, and Bassey Ogbonnaya
AI-Powered Compliance: Accelerating efficiency and decision-making for Compliance related inquiries.
Amberly R. Rodriguez
Predictive Analytics in Public Health: Developing AI Strategies to Combat High-Temperature Impacts on Violence and Overdoses in Las Vegas
David Camacho, Stephanie Duarte, Kosi Okeke, Chris Papesh, and Jacquelyn Cheun-Jensen
Predicting Simulation Times for Multiphase Thermal-Hydraulic Models
Andrew Yule and Andrew Taylor
Mobile Computer Vision Application for Agricultural Disease Detection of Pepper Diseases using Two-Stage Deep Learning System
Carlos Jose Estevez, Mai Dang, and Ryan Bass
