Generalized framework for Prescriptive Machine Learning using IoT datastreams.

Internet of things (IoT) includes of multitude of sensors from a wide variety of applications. These sensors produce high volume and high velocity data. Recently there has been much interest in application of such technologies to improve energy management and agricultural practices. The sensors that are installed in the field transmit real time data regarding numerous environmental variables of interest. This data is then used to forecast a future state and to make a well informed business/operation decision according to an expected future state. One of the challenges in application of such technology is to improve prediction accuracy of the forecast without compromising operating cost or the time it takes to make a prediction. This project will design a generalized framework based on machine learning and deep learning methodologies to improve prediction accuracy.

Intern: 
Fazal Momin
Faculty Supervisor: 
Pawan Lingras
Province: 
Nova Scotia
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