Intelligent Analytics for Dynamic Events in a Smart City

Artificial Intelligence (AI) research has grown rapidly in recent years as the result of faster computers and better algorithms. AI models can be trained to automate the decision process and provide results. However, if the model is not properly or sufficiently trained, the outcome will likely be unpredictable and inaccurate. Besides, training data is not easily available in a lot of applications. To address these issues, our strategy is to integrate classical Computer Vision (CV) algorithms and Deep Learning (DL) techniques. CV can provide solutions without training data. CV knowledge is also valuable to select significant features, which is necessary to train AI model. Our strategy takes into account of the trade-off between CV and DL, and can benefit a wide range of applications, including healthcare data analytics, event monitoring and pattern classification in general. The objective of this proposal is to support intelligent business operations in a smart city environment.

Intern: 
Harsh Sharma;Frincy Clement;Harshal Soni;Jatin Dawar;Xinli Cai
Faculty Supervisor: 
Irene Cheng
Province: 
Alberta
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