Statistical and Physiological Beat Modelling of Seismocardiogram Signal

"Seismocardiogram (SCG) is a signal that is captured by placing an accelerometer on the human chest. This signal captures very important timing information such as opening and closing of the heart valves. In addition to these timing information, the non-invasive nature of this signal makes it an attractive solution for remote monitoring of patients with heart conditions.
The morphology of SCG signal changes depending on different types of heart conditions and diseases. A mathematical model represents the morphology of a signal in terms of certain parameters.

Deconvolution of Whole Blood Transcriptome based on mRNA-Seq data

Gene expression in blood is highly affected by the type and proportion blood cells. Therefore, cell composition needs to be taken into account when looking for signatures specific to a condition. The issue is that cell composition needs to be assessed on fresh blood, i.e. at time of blood collection. If this has not been done, the only way one can assess is by predicting it using a methodology suggested in this proposal. Therefore, if blood cell count is not available, the cell composition can be inferred from existing next generation sequencing data sets.

A statistical method for competing risk survival analysis with clustered big data

Over the last few years, the data revolution occurred with the emergence of “Big data”. In medical field, the term big data refers to large databases in terms of patients and/or information from varied sources. Nevertheless, heterogeneity is encountered in this kind of data. Indeed, data arise from different medical centers. Furthermore, we can’t perform traditional statistical methods on these large databases: major problem are multicollinearity and overfitting. Lots of regularization methods have been proposed in order to adapt classical methods. Mittal et al.

A new method for educational assessment: measuring association via LOC index

Our objective is to develop the new technics, based on the pioneers’ work, especially the Qoyyimi and Zitikis’s (2015) extension, which can have a good performance on measuring some kinds of relationship between students’ marks of subjects. In order to understand the relationship between students’ marks on different study subjects, many studies apply some kinds of indices, such as the Pearson correlation to measuring association between variables of interest in a variety of research areas, including education. We do some extension on this route.

Do certain aspects of national culture make corruption more prevalent in some countries?

Corruption is recognized as a major factor hindering the development and advancement of countries where it is prevalent. (By “corruption”, I specifically mean “political corruption”, “the use of powers by government officials for illegitimate private gain” [Wikipedia].) Even where the incidence of corruption is relatively low, corruption when exposed has had dramatic effects, including changes in government as the majority of voters indicated their strong disapproval—this has been the recent history in Canada and particularly in Quebec.

Changes in Fire Incidence and Area Burned Under Climate Change Scenarios

New methods and tools are required to (i) provide insight into spatio-temporal climate effects concerning forests and fire events (ii) provide policy-makers with quantified estimates of fire activity under changing climate scenarios as well as well-defined historical models for seasonality and the occurrence of extremes (iii) be useful in a wide variety of modeling scenarios which investigate extremes and climate change effects (iv) be disseminated broadly and hence provide forest researchers with key information on climate effects (v) provide substantial interdisciplinary expertise to the st

Modeling User Behaviour Over Time from Chat Messages

As online communication among children and young adults grows in popularity, concerns about online safety are receiving vast attention. The Two Hat Security (2HS) company has a rule-based filtering system to detect malicious chat messages and identify abusive users. The proposed project will improve the predictions of the trustworthiness (trust-level) of the users which can change over time and also influences what users are allowed to say in chat messages.

Risk Analysis and Efficiency Measurement of Protocols for Missing Children in Saskatchewan

We aim to use Saskatchewan’s data and current protocols to explore and identify the risk factors for the challenge of investigating cases of missing children. We will be using a variety of analytical methods in order to come up with a set of valuable recommendations for improving the process of investigating the cases of missing children.

Long term modelling of power prices

Power prices are a significant contributor to the overall risk of almost any large-scale industry. In particular, energy companies such as TransAlta who are active participants in many regional power markets have a strong interest in understanding the long-term risks they are exposed to. This project seeks to develop a model that will help TransAlta to understand some of the uncertainty in medium  to long-term power prices in California, the Pacific northwest, and Alberta.

Investigate machine learning algorithms to detect anomalies incomputing infrastructures in real-time

Metafor is developing a new class of IT system management solution to monitor computer and application activities, and alert when anomalous behavior occurs. Current commercial tools for anomaly detection use simple statistical rules and thresholds to detect anomalies. These methods are failing for today’s dynamic cloud environment where change is constant. As a result, IT operators are flooded with false alerts; become overwhelmed with alert fatigue and learn to ignore the alerts.