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Reviewed by Emily Henderson, B.Sc.Nov 20 2020
Emergencies, by their very nature, are hard to predict. When and where the next crime, fire, or vehicle accident will happen is often a matter of random chance.
What can be measured, however, is how long it takes for emergency services personnel to consider a particular incident to be resolved — for instance, suspects apprehended, flames extinguished, or damaged cars removed from the street.
New York City is among the large urban areas that maintain those kinds of statistics, and a team of researchers at Binghamton University, State University of New York has used deep-learning techniques to analyze the numbers and suggest improved public safety through re-allocation of resources.
Arti Ramesh and Anand Seetharam — both assistant professors in the Department of Computer Science at the Thomas J. Watson College of Engineering and Applied Science — worked with Ph.D. students Gissella Bejarano, MS ’17, and Adita Kulkarni, MS ’17 (who earned her doctorate earlier this year), and master’s student Xianzhi Luo to develop DeepER, an encoder-decoder sequence-to-sequence model that uses Recurrent Neural Networks (RNNs) as the neural network architecture.
The research utilized 10 years of publicly available data from New York City’s five …
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