Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

31 620 projets complétés

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Projets par catégorie

Assessing the impacts of climate change on insurance sectors, public safety, and economic sustainability in North America: A Market Intelligence Review and Meta-analysis

Climate change has been projected to increase the frequency and severity of extreme weather (wildfire) and increase the prevalence of vector-borne infectious diseases (such as West Nile Virus and Lyme disease), which will potentially affect the insurance sectors through increased claims, financial losses, and the development of uninsurable risks. This review will follow a rapid scoping review format to synthesize evidence efficiently.

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Superviseur du corps professoral :

Thomas Cooper;Atanu Sarkar

Étudiant :

Partenaire :

Clear Risk Inc.

Discipline :

Business

Secteur :

Professional, scientific and technical services

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Development of computer vision framework for different technology platforms

Implementation of low cost techniques for automation is critical for various industries. At Fourien, we are developing various types of analytical instruments and robotic solutions for a variety of clients. Instead of installing multiple sensors in our technologies, we can employ computer assisted image recognition to implement various levels of automation in our different platforms at a lower cost and higher efficiency. As the cost of high resolution cameras is coming down, computer vision and image processing is becoming more accessible. Therefore, we would like leverage this opportunity to introduce the image recognition capability in our technologies, in order to make our technologies more effective and affordable.

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Superviseur du corps professoral :

Li Cheng

Étudiant :

Partenaire :

Fourien

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Alberta

Programme :

Accelerate

Software Programming

Currently, a gap exists between basic and applied research in Streamlined Health Care Delivery. Using AI, and QDoc software we would like to work closely in collaboration with the researchers to develop AI tools that work on Streamlining the Primary Care between Physician and Patient. To be able to do that, it requires access to large amounts of clinical data, which can be used to train and validate algorithms.

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Superviseur du corps professoral :

Cuneyt Gurcan Akcora

Étudiant :

Partenaire :

QDoc

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology

Université :

University of Manitoba

Programme :

Business Strategy Internship

Implementation of a Balanced Scorecard for Organization-Level Performance Measurement for Family Services Ottawa

FSO (Family Services Ottawa) is a not-for-profit social service agency whose mission is to assist individuals, to build on their strengths and to improve their mental health. FSO does so by providing counselling, education, and advocacy within a context of working to foster systemic change. Although their programs are increasingly monitored and evaluated at the program level, FSO does not currently have a tool to assess and monitor their organization as a whole. This project will identify the most critical measures for monitoring and developing a strategic direction for the organization, balancing theories of strategy with the practical needs related to executing strategy. The intern will work with a working committee of relevant stakeholders and organization members and will consult with various internal organizational committees to create a balanced scorecard for organization-level performance measurement that the organization can use to demonstrate effectiveness to funders and improve organizational functioning.

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Superviseur du corps professoral :

John Sylvestre;Tim Aubry

Étudiant :

Partenaire :

Family Services Ottawa

Discipline :

Sociology

Secteur :

Health and Related Sciences & Technology

Université :

University of Ottawa

Programme :

Business Strategy Internship

Predictive microsimulation model for knee-OA progression and cost-effectiveness of earlynon-pharmacological therapies to improve long term patient outcomes

The proposed study has three objectives: Objective 1. Develop KOA microsimulation model: To develop and validate a Knee-OA population-based microsimulation model (POHEM-KOA) using the previously developed POHEM-OA model. Objective 2. Perform cost-effectiveness analysis of non-pharmacological therapies for KOA: To use POHEM-KOA in conducting a cost-effectiveness analysis of treatment strategies, including exercise therapy and weight reduction, targeted at obese and overweight early OA patients in Canada over the next 20 years [2013, 2033]. Objective 3. Develop simulation-based risk calculator software (knowledge transfer): To develop ‘Ko-Ask’, a clinical simulation-based risk calculator software that is capable of predicting OA progression outcomes for those diagnosed with early OA in Alberta, Canada. Methods and Study Design Below we describe the research plan corresponding to each study objective. Objective 1. Developing POHEM-KOA The evolution of several risk factors for OA, including BMI, physical activity (PA), Job type/length and OA incidence have already been implemented inside POHEM models (Appendix 1). We will adjust these for KOA estimates based on sex and age specific KOA proportions reported in (9). Once an incident KOA event is assigned, the patient will enter the “progression module”.

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Superviseur du corps professoral :

Deborah Marshall

Étudiant :

Partenaire :

University of Calgary

Discipline :

Life Sciences

Secteur :

Education

Université :

University of Calgary

Programme :

Elevate

Développement d’algorithmes et de méthodes de visualisation automatique de données dans le cadre d’un outil d’aide à la décision en milieu scolaire

L’objectif général de ce projet de recherche est d’étudier des approches statistiques et computationnelles pour permettre l’analyse automatique des données structurées et non-structurées dans le contexte de décision péda-gogique et dans le cadre d’un outil tel que Reussito. Afin d’étudier la pertinence de ces approches, le projet se concentrera sur le développement des algorithmes d’analyse du langage naturel à partir des commentaires écrits concernant les apprentissages des étudiants ainsi que les notes de ceux-ci. Le projet consiste en l’étude des méthodes d’analyse du langage naturel ainsi que des méthodes de visualisation des données combinant les enjeux détectés dans les commentaires ainsi que les notes afin d’aider la prise de décision de la part du corps enseignant et de la direction

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Superviseur du corps professoral :

Jean-François Plante

Étudiant :

Partenaire :

Videns Analytics

Discipline :

Sociology

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

HEC Montréal

Programme :

Accelerate

Parcours d’insertion et de réinsertion aux études des jeunes à risque : quelles sources de soutien contribuent à promouvoir la persévérance lors de la transition à l’âge adulte ?

Ce projet cible une population sous-représentée dans les études sur la transition à l’âge adulte, soit les jeunes abandonnant l’école avant la diplomation du secondaire. Il vise à approfondir le portrait de leurs parcours scolaires post-décrochage, auprès d’un échantillon de 545 jeunes provenant de milieux socioéconomiquement désavantagés, interviewés une fois à l’adolescence, puis une seconde fois à l’âge adulte. Deux questions prioritaires pour Réseau Réussite Montréal (RRM), seront abordées:
1. Pour les jeunes qui réussissent à s’engager ou se réengager dans leurs études de manière à obtenir leur diplôme secondaire (p.ex. : via la Formation générale aux adultes) pour ensuite poursuivre vers le CÉGEP ou l’emploi : quelles sources de soutien social, communautaires et institutionnelles ont contribué à leur réussite?
2. Pour ceux qui peinent à obtenir le diplôme convoité et à s’insérer en emploi : quelles ressources auraient pu les aider? Pourquoi n’y ont-ils ou elles pas eu recours?

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Superviseur du corps professoral :

Véronique Dupéré

Étudiant :

Partenaire :

Réseau Réussite Montréal

Discipline :

Sociology

Secteur :

Other services (except public administration)

Université :

Université de Montréal

Programme :

Accelerate

Revealing Trends in Quantum Technology with Natural Language Processing and Deep Learning Techniques

Recent years have witnessed a surge of interests in exploring emerging research directions of quantum technology, including quantum computing, quantum sensors, quantum cryptography, etc., and the number of scientific publications in the literature in this field has been growing steadily. However, due to the large volume of unstructured text data and the lack of automatic archiving of the obtained results, much of the information remains buried in textual details and can not be fully exploited for further usage and analysis. To meet this challenge, this project explores natural language processing (NLP) and deep learning (DL) methods to automatically discover useful patterns from such publications. More specifically, the project focuses on automatically retrieving academic papers, e.g., held in arXiv, Google Scholar, etc., summarizing large scale text collections and discovering ongoing hot topics across quantum tech, and presenting the insights on evolving trends back to end users. The discovered knowledge may help quantum tech companies, investors and governments identify future directions and trends, and ultimately gain a competitive advantage.

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Superviseur du corps professoral :

Yang Liu;Xu Sunny Wang

Étudiant :

Partenaire :

The Quantum Daily Inc.

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Wilfrid Laurier University

Programme :

Accelerate

Machine learning approach to monitoring and decarbonizing mineral processes

In Canada’s 2030 Agenda for Sustainable Development, one of the goals is to take urgent action to combat climate
change and its impacts. According to this agenda, the target is to reduce Canada’s total greenhouse gas (GHG)
emissions by 30%. In order to transition to renewable energy, solar and wind power in combination with energy
storage such as lithium-ion batteries can be used to replace fossil fuels. Although this is approach has a great
potential in decreasing the carbon emissions in the long-term, it is also important to minimize the environmental
effects of the production of these batteries. The proposed research aims to reduce carbon emissions in mineral
processing activities using machine learning. An adaptable machine learning model will be developed to minimize
fuel consumption by processing plants. This will allow increasing the production of required minerals for lithiumion
batteries in Canada while meeting the GHG reduction targets.

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Superviseur du corps professoral :

Yuksel Asli Sari

Étudiant :

Partenaire :

Emily Thorn Corthay Inc.

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

Queen's University

Programme :

Accelerate

Blockchain Financial and Risk Modeling with Tensor Decompositions and Deep Learning for Institutional Investors

Cryptocurrencies are a new asset class that are emerging in importance and adoption in global markets. Public blockchains such as Bitcoin and Ethereum offer access to an unprecedented amount of financial and technical data. With the increasing amount of blockchain data, it is important that investors and policy makers can access standardized methods of financial and risk analysis to be able to make informed investment and regulatory decisions. Blockchain data are often difficult to model. Tensor decompositions have previously been integrated with deep learning models to successfully predict stock and spot prices. However, there exists a gap in the literature of how to model cryptocurrency spot prices and derivatives. The primary objective of this project is to create and compare two tensor-based deep learning models in each of the two key areas: 1) Cryptocurrency Price Dynamics Modeling, and 2) Derivatives Price Dynamics Modeling.

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Superviseur du corps professoral :

Victoria Lemieux;Elina Robeva

Étudiant :

Partenaire :

AquaNow

Discipline :

Mathematics

Secteur :

Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Accelerate

Design and Development of Collaborative Robots for Greenhouse Harvesting

Collaborative robots, also known as Cobots, are devices that will help farmers in their farming duties to have better control over their crops, increase harvesting efficiency, and solve labor shortage issues in rural areas. These robots can operate near humans and complete a wide variety of tasks including harvesting crops, precise pesticide spraying and crop monitoring. Employing robots in agricultural settings is challenging, however, due to the variability in the environment they work in resulting in complex functional requirements. To safely operate near humans with any autonomy, these robots must have complex machine vision systems and sensors where a series of sensors connected to an on-board computer provide continuous feedback from the robot’s surroundings and the end gripper allowing the robot to collect data and complete its tasks safely. This project aims to develop a cobot using a robotic manipulator as a greenhouse harvesting device. Machine vision algorithms will be designed to allow the robot to navigate its space, identify objects and orient itself correctly. A compliant custom gripper with crops proper interface loadings will also be designed to provide proper grip while avoiding crops damage.

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Superviseur du corps professoral :

Mostafa El Sayed

Étudiant :

Partenaire :

ASP Laser Inc

Discipline :

Engineering

Secteur :

Technology; Manufacturing and Construction; Other

Université :

Carleton University

Programme :

Accelerate

Federalism and Politics of Compromise

This study will seek to build a normative theory of compromise and federalism. Several questions will serve as guidelines for our research: If federalism does depend on a specific type of compromise, what kind of compromise are we talking about? Would federalism be a normatively acceptable form of compromise?
Several analytical differences will be explored: federalism and subsidiarity, on the one hand, conflict and cohesion, on the other hand, and finally, justifications for federalism and the Nation-State.
This research project will be structured as follows: 1 – Analysis of problems and types of compromises giving rise to federal structures (risk of wars, conflicts between minorities, lack of recognition…); 2 – Possible solutions of federalism as a method of compromise; 3 – Factors maintaining the stability of the chosen solution (trust; cohesion etc.) and their relationship to compromise.
Its aim is to 1 – clarify the relationship between federalism and compromise 2 – refine our understanding of social cohesion and trust, as a preliminary condition for compromise, or effect of it and 3 – highlight instruments mobilized to ensure the stability of federal compromises.

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Superviseur du corps professoral :

Daniel Weinstock

Étudiant :

Partenaire :

Ecole des hautes études en sciences sociales

Discipline :

Sociology

Secteur :

Education

Université :

McGill University

Programme :

Globalink Research Award

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Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.