The main objective of the project is to upgrade the existing system at Cheetah Networks to make use of Canadian cellular CAT M1 monitored network data to develop innovative QoE analytics that can be used to provide actionable insights. The system will explore applying new techniques to capture in real-time QoE visibility into experiences locally, regionally and nationally. The primary methodologies that we will be employing are based on machine learning and deep learning techniques for data classification, clustering and analysis.
Women are disproportionately affected by neuro-degenerative disorders compared to men. For years, research has attempted to identify why this phenomenon occurs. The answer may be found in the gut. The intestinal tract contains millions of bacteria that are colonized from birth; These bacteria are essential for keeping the brain and immune system healthy. Changes in the proportions of bacterial species during critical periods of development can have lasting impacts on neuro-inflammation and degradation.
Research into child safety applications using Artificial Intelligence (AI) methods is a new area of investigation. SafeToNet is continuing to develop AI monitoring tools together with a team of researchers at the University of Ottawa. These tools, when used over time, will take advantage of outgoing text-based communications from devices to detect the early onset and progression of developmental and mental health issues in youth.
Lead scoring is essential for lead management. The result of lead scoring is a list consists of leads with scores assigned indicating how likely each lead can be converted into the next stage of sales process. The Lamb or Spam and the Rule-Based are the two lead scoring methods that have been discussed in the literature. As various machine learning algorithms and artificial intelligence started to reemerge, predictive lead scoring models seem to be the next promising solution for lead scoring activity.
With the increasing popularity of digital assets such as cryptocurrencies, many financial technology (FinTech) systems have become safety critical. However, current FinTech system development approaches often lack the rigorous safety practices found in the aerospace, nuclear, automotive, and military industries.
Deep learning in computer vision has set new standards in mobile and web-based applications. The power of learning-based computer vision has also tremendous potential in machine vision. Traditionally, machine vision in manufacturing employs analytic solutions often resulting in excellent accuracy but poor robustness. The goal of this project is to increase robustness of a vision-based measurement process in sheet metal manufacturing using deep learning.
The use of fossil fuels for energy has led to the significant emission of greenhouse gases from the stationary and automobile sources. Methane (CH4) is an abundant source of fuel found in large quantities in natural gas reserves or produced synthetically is an alternative fuel for motor vehicles, large track transportation, marine application because of its low carbon emission per energy produced. However, methane is a potent green house gas and needs to be fully converted to CO2 to prevent its release into the atmosphere.
This research helps to automate ontology development in order to support semi-supervised and active learning chatbot as much as possible, so that the overhead of chatbot training that requires human supervision is minimized, while relevant knowledge management activities become more efficient. The research objectives are both to refine the quality of the chatbot interactions and to automate its development and training as much as possible, to implement and test its practical and cost saving capabilities in tourism industry.
Wave surfing was traditionally restricted to oceans. Nowadays it is becoming more and more popular in rivers. Although there are some natural surfable waves in rivers, human constructions can provide waves even where they do not naturally exist. However, there is not enough academic support for design and construction of these waves. In this project, the research intern utilizes physical river models to simulate river surfing waves. The industrial partner defines the existing problems and ideas of wave construction which is a result of 10 years of construction experience around the world.
A risk-based approach to anonymization includes an assessment of the risk that an attack to reveal or uncover personal information will be realized, known as threat modelling, against the risk that an attack on the data will be successful (e.g., a re-identification). We wish to incorporate the provable guarantees of differential privacy into this assessment of risk, to produce safe data in context of the environment in which it will be used. We also need adapt the methods of statistical disclosure control to such an updated approach.