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GADGET - Online Gambling Addiction Detection, 2019

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GADGET - Online Gambling Addiction Detection, 2019

This project proposes an AI (Artificial Intelligence)-based tool that could capitalize on the vast amount of data collected every day and analyze online user behavior to model and detect the behaviors associated with addicted gamblers.

The problem represents a major challenge for a few reasons: first, the massive amount of data involved (that will require efficient data- analysis algorithms); second, the temporal dimension of the phenomenon we intend to model; and third, the fact that we are trying to observe and affect behaviors associated with a very small fraction of the population. The analysis confronts an additional complication by virtue of the potentially infinite behaviors associated with various kinds of gamblers.

To tackle this problem, we propose a system based on a version of Recurrent Neural Networks (RNNs) the architecture of which will be optimized by a neuroevolution algorithm.