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Full-Text Articles in Engineering
Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan
Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan
SMU Data Science Review
In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …
Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels
Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels
SMU Data Science Review
As the digital age creates new ways of spreading news, fake stories are propagated to widen audiences. A majority of people obtain both fake and truthful news without knowing which is which. There is not currently a reliable and efficient method to identify “fake news”. Several ways of detecting fake news have been produced, but the various algorithms have low accuracy of detection and the definition of what makes a news item ‘fake’ remains unclear. In this paper, we propose a new method of detecting on of fake news through comparison to other news items on the same topic, as …