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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696
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899
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9419
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98
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619
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Projets par catégorie

Forum Representation for Cross-Domain Recommendation

An internet forum is an online site for people to have conversations. It contains threads to hold discussions between users. Recommending appropriate threads to forum users is one of the main goals of an internet forum. To provide positive user experience, cross-domain thread recommendation is required, which can be benefited greatly from the help of forum representations. This research project aims to use two different approaches to create forum representation. One approach is to use the content-based method that utilizes textual data in each subforum and build a topic model to generate subforum embedding vectors. Another approach is the user-based method. It generates subforum embeddings by using a modified skip-gram model, which uses the subforum to predict its user contexts. Lastly, the research project will explore the possibility for a hybrid user-content based approach to further increase the embedding performance.

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Superviseur du corps professoral :

Gerald Penn

Étudiant :

Partenaire :

VerticalScope

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Private Sector’s Role in Chronic Disease Management in Kyrgyzstan

This study will systematically review the role of the private sector in the management of NCDs (including cardiovascular disease, cancer, etc.) in developing countries, and its implications to Kyrgyzstan. We will try to understand how low- and middle-income countries with similar GDP per capita, disease burden, and health system needs have harnessed the capacity of private sector in the management of NCDs. Experiences gained from these exemplar country(ies) will be emphasized and suggestions on how this could be applied to the Kyrgyzstan context will be provided. The outputs of this study will provide much-needed data, evidence and policy recommendations for our partner organizations, and for Canadian healthcare industry and pharmaceutical companies to enter this market, and potentially other similar markets in Central Asia countries.

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Superviseur du corps professoral :

Zulfiqar Bhutta

Étudiant :

Partenaire :

Aga Khan Foundation Canada

Discipline :

Life Sciences

Secteur :

Other services (except public administration)

Université :

University of Toronto

Programme :

Accelerate

New Method for Delivering Protectants into Honey Bee Hives

Pollination services provided by honey bees is imperative to crop production in Canada. Unfortunately, honey bee health is compromised by pests and diseases leading to the decline and/or death of hives and decreased pollination. In this project we will implement the usage of a special inspenser to bring in materials that will protect the honey bees from pests and diseases. These inexpensive inspensers will decrease the workload of already busy beekeepers, while also improving the overall health of the hives. Our partner organization, George Weston Limited, is the largest grocery retailer in Canada, providing food to over 34% of Canadians. Healthy beehives provide better pollination resulting in more food to be delivered to Canadian grocery stores at a lower cost to farmers, beekeepers, and grocery store retailers.

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Superviseur du corps professoral :

Peter Kevan

Étudiant :

Partenaire :

George Weston;Best for Bees

Discipline :

Life Sciences

Secteur :

Manufacturing; Retail trade

Université :

University of Guelph

Programme :

Accelerate

Machine Learning for Cancer Treatment Plan Benchmarking

Oncology specialists are few in numbers and cannot be present within every hospital or clinic providing their guidance and support. There are institutions in this world with oncology experts that can provide the best possible care and the knowledge of these experts is stored in medical case files within the hospital. With the power of machine learning and the internet, an organization without any experts can compare their cancer treatment plans to the top performing institutions in the world and receive constructive feedback on where their plan needs improvement. Bridge7 AI is developing a platform that allows clinicians around the world to compare and improve their plans with the help of experts in all medical fields.

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Superviseur du corps professoral :

Marzyeh Ghassemi

Étudiant :

Partenaire :

Bridge7

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Applied Machine Learning for Early Detection of Retinal Toxicity

Hydroxychloroquine (HCQ) is an anti-inflammatory drug that is widely prescribed for a range of auto-immune disorders such as lupus and rheumatoid arthritis. An unwanted side effect of long-term use of HCQ is vision loss by retinal toxicity. If detected early, it could lead to early intervention to prevent vision loss and improve the quality of life for patients.
The project involves research on current machine learning approaches for the development of a system that would aid in the early detection of retinal toxicity. Current approaches involve qualitative interpretation of multifocal electroretinogram (mfERG) and optical coherence tomography (OCT) images by an expert. The project aims to develop a system that automates the interpretation of mfERG and OCT images to assist medical professionals in making an accurate diagnosis.

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Superviseur du corps professoral :

Arvind Gupta;Huaxiong Huang

Étudiant :

Partenaire :

Kensington Eye Institute

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology

Université :

University of Toronto

Programme :

Accelerate

PGX-processed yeast beta-glucans as an inhalable immunomodulating therapeutic for COVID-19 patients

While many people who acquire COVID-19 experience only minor symptoms or are completely asymptomatic, others (~20% of patients) experience a severe form of the disease associated with a phenomenon called a cytokine storm, an undesirable immune response that ultimately results in the deposition of fibrotic tissue in the lungs that causes the breathing difficulties and ultimately death in severe COVID-19 cases. There are to-date no therapeutics that have been demonstrated to relieve such cytokine storms, or avoid the resulting changes in the lung tissue, observed in severe COVID-19 patients. In this project, we seek to expand on preliminary results from an ongoing collaboration between the labs of Kjetil Ask and Todd Hoare at McMaster University and our industry partner Ceapro regarding the utility of yeast beta-glucan particles processed using Ceapro’s pressurized gas expanded liquids (PGX) technology for modulating the immune system in without any added drug. PGX processing both purifies and expands the raw yeast beta-glucan product to both remove components that can cause undesirable side-effects and reduce the density of the material to make the particles easier to inhale directly into the targeted lung tissue.

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Superviseur du corps professoral :

Kjetil Ask;Todd Ryan Hoare

Étudiant :

Partenaire :

Ceapro

Discipline :

Life Sciences

Secteur :

Manufacturing

Université :

McMaster University

Programme :

Accelerate

The Short- and Long-Term Impacts of the COVID-19 Pandemic on Ontario Manufacturers, Manufacturing Employees, and Supply Chains

The COVID-19 pandemic has had an unprecedented impact on Canada’s economy. More specifically, it has:
1) exposed deficiencies in supply chains, causing manufacturers to pivot production in order to improve the supply of essential goods (e.g. medical devices, personal protective equipment, sanitizer),
2) demonstrated the important role that manufacturing plays in a well-functioning economy and society, and
3) brought to light the lack of comprehensive information related to Canadian manufacturers capabilities.
This project attempts to address questions related to the role of manufacturers in mitigating the impacts of the COVID-19 pandemic, better understanding the capabilities of manufacturers (especially as they relate to the production of essential goods), and ensuring that government policies and programs designed to support manufacturing during the anticipated economic recovery and during future crises are effective.

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Superviseur du corps professoral :

Gregory Zaric

Étudiant :

Partenaire :

Trillium Network for Advanced Manufacturing

Discipline :

Business

Secteur :

Information and cultural industries

Université :

The University of Western Ontario

Programme :

Accelerate

An Investigation of Service Mesh(es) and Security Models Within and Across Multiple Distributed Systems

Global service providers in highly regulated financial sectors must accommodate an ever-changing, sometimes competing, landscape of regulatory concerns. This project seeks to determine a reasonable path forward in technology design and adoption to accommodate current and anticipated infrastructure changes. Moreover, bridging the service layer across multiple, distinct distributed systems of varying complexity will pose new challenges while performance and observability of these systems become critical consideration. Parallel architectural patterns require multiple service integration points and the ability to negotiate the movement of data securely. The objective is to review the industry landscape of service meshes and/or cryptographic patterns for suitable accommodation of multiple system architectures. Specific targets for latency (with defined upper bounds) and parity performance for read and write rates must be met. Adjustments based on the assumption of network behaviour are expected to be addressed, including failure conditions and stale data concerns, where applicable.

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Superviseur du corps professoral :

Ashvin Goel

Étudiant :

Partenaire :

Ethoca Technologies

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Explore efficiently automated parallel hyperparameter search for optimizing machine learning models over large scale cloud cluster

Machine learning has been applied in various fields and shown promising results in recent years. Researchers have found that tuning machine learning models in a proper way can vastly boost the model performance with respect to the specific AI task. However, tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming. There is therefore great appeal for automatic approaches that can optimize the hyperparameter of any given model. This project aims to provide an end to end automotive hyperparameter search framework that can help people explore better machine learning models

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Superviseur du corps professoral :

Gennady Pekhimenko

Étudiant :

Partenaire :

Layer 6 AI

Discipline :

Computer science

Secteur :

Information and Communications Technology; Technology

Université :

University of Toronto

Programme :

Accelerate

Road Accident Severity Detection using Telematics and Environmental Data from Connected Vehicles

Road safety affects everyone, not just Geotab customers. With several years of driving and environmental data collected from over 2 million connected vehicles, there is a great opportunity to leverage big data and machine learning to establish an accident detection system. On top of driving data and environmental data, it also contains machine diagnostic data which is hypothesized to have highly correlated features when it comes to accident detection. As such, the objective is to use this to detect the severity of an accident through a combination of ML approaches which fall under the umbrella of supervised, semi-supervised, and unsupervised learning. Based on our findings, both Geotab’s customers and the entire community will benefit from it as the end goal is to use this as a proactive measure for their clients and the respective city planners.

Voir la description complète du projet
Superviseur du corps professoral :

Marsha Chechik

Étudiant :

Partenaire :

Geotab Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

Université :

University of Toronto

Programme :

Accelerate

Interpretable Machine Learning for Predictive Analytics in Employee Benefits Insurance

In recent years, many machine learning methods have been developed for predictive analytics and automated decision making. However, the lack of explanation resulted in both practical and ethical issues. In this project, we will employ and advance interpretable machine learning methods for various predictive analytics tasks in employee benefit insurance. The proposed methods can be used by the partner organization to improve transparency and hence trust in a wide range of applications that involve predictive analytics.

Voir la description complète du projet
Superviseur du corps professoral :

Majid Komeili

Étudiant :

Partenaire :

Global IQX

Discipline :

Computer science

Secteur :

Finance and Insurance

Université :

Carleton University

Programme :

Accelerate

Described Video and Language Detection on Audio-tracks Using Machine Learning

Bell Media receives content from different providers, including content it produces in-house. There are standards for tagging audio tracks with metadata however many facilities (including Bell) do not adhere to these standards. Currently Bell uses a manual approach to classify unlabeled audio tracks, which is inefficient, and time consuming for massive digital media that Bell has and receives. Bell is developing a single ingest pipeline to accelerate the labeling and processing of media files it receives. This research project will look at two features that Bell Media would like to include in the ingest pipeline. The first feature is the ability to automatically classify the audio track of the media file into its language type, primarily English or French. The second feature is to identify which audio track carries the described video information. In this research we will develop machine learning solutions for these two problems.

Voir la description complète du projet
Superviseur du corps professoral :

Shahram Shirani

Étudiant :

Partenaire :

BCE Inc

Discipline :

Engineering

Secteur :

Information and cultural industries

Université :

McMaster University

Programme :

Accelerate

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