DeepER tool uses deep learning to better allocate emergency services

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BINGHAMTON, NY — 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 PhD 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 boroughs, broken down by categories …

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