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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 2 (2019)

Articles

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Improve Image Classification Using Data Augmentation and Neural Networks
Shanqing Gu, Manisha Pednekar, and Robert Slater

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Forecasting Localized Weather-Based Photovoltaic Energy Production
Kevin Chang, Afreen Siddiqui, and Robert Slater

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Visualizing United States Energy Production Data
Bruce P. Kimbark, Melissa Luzardo, Charles South, and James Taber

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Longitudinal Analysis with Modes of Operation for AES
Dana Geislinger, Cory Thigpen, and Daniel W. Engels

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A Personalized Approach to Understanding Human Emotions
Gregory A. Lazenby, Kim Wong, and Daniel W. Engels

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AWS EC2 Instance Spot Price Forecasting Using LSTM Networks
Jeffrey Lancon, Yejur Kunwar, David Stroud, Monnie McGee, and Robert Slater

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Machine Learning Predicts Aperiodic Laboratory Earthquakes
Olha Tanyuk, Daniel Davieau, Charles South, and Daniel W. Engels

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Predicting Premature Birth Risk with cfRNA
Jason Lin, Jonathan Marin, and John Santerre

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Identifying Undervalued Players in Fantasy Football
Christopher D. Morgan, Caroll Rodriguez, Korey MacVittie, Robert Slater, and Daniel W. Engels

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Automated Pleural Effusion Detection on Chest X-Rays
Nathan Wall, Muthu Palanisamy, and John Santerre

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Machine Learning in Support of Electric Distribution Asset Failure Prediction
Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, and Jade Thiemsuwan

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Predicting Wind Turbine Blade Erosion using Machine Learning
Casey Martinez, Festus Asare Yeboah, Scott Herford, Matt Brzezinski, and Viswanath Puttagunta

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A Machine Learning Model for Clustering Securities
Vanessa Torres, Travis Deason, Michael Landrum, and Nibhrat Lohria