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The SMU Data Science Review is a peer-reviewed 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 2, Number 1 (2019)

Articles

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Postsecondary Attainment: Identifying Areas to Improve Retention for North Carolina Community Colleges
Noelle Brown, John Heinen, Matthew Rega, Lizzy Sterling, and Jacob Drew

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Machine Learning vs Conventional Analysis Techniques for the Earth’s Magnetic Field Study
Sheri Loftin, Sarah J. Fite, Laura V. Bishop, and Stavros Kotsiaros

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Automate Nuclei Detection Using Neural Networks
Jonathan Flores, Thejas Prasad, Jordan Kassof, and Robert Slater

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Machine Learning Pipeline for Exoplanet Classification
George Clayton Sturrock, Brychan Manry, and Sohail Rafiqi

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Visualization and Machine Learning Techniques for NASA’s EM-1 Big Data Problem
Antonio P. Garza III, Jose Quinonez, Misael Santana, and Nibhrat Lohia

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Repairing Landsat Satellite Imagery Using Deep Machine Learning Techniques
Griffin J. Lane, Patricia Goresen, and Robert Slater

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Leveraging Reviews to Improve User Experience
Anthony Schams, Iram Bakhtiar, and Cristina Stanley

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Network Traffic Behavioral Analytics for Detection of DDoS Attacks
Alma D. Lopez, Asha P. Mohan, and Sukumaran Nair

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KadAfrica: Survey Analysis to Support Research for Smallholder Farmers
Gregory Asamoah, Robert Gill, Frank Sclafani, and Bivin Sadler

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Demand Forecasting: An Open-Source Approach
Murtada Shubbar and Jared Smith

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ASL Reverse Dictionary - ASL Translation Using Deep Learning
Ann Nelson, KJ Price, and Rosalie Multari

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Identifying High Risk Patients for Hospital Readmission
Ethan Graham, Asha Saxena, and Heather Kirby

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Optimize the Effectiveness of Recruiting Campaigns
Ryan A. Talk, Lakshmi Bobbillapati, and Marshall Coyle