Projets novateurs réalisés

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

29 670 projets achevés

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Projets par catégorie

Alison Xia – Artificial Intelligence in Modern Hospitality

Artificial Intelligence in Modern Hospitality will analyze the effects and business applications of AI in various aspects of the hospitality industry, including but not limited to food & beverage, travel & tourism, lodging, and recreation. Specifically, it will analyze how AI fits into various consumer profiles, allowing for greater flexibility, efficiency, and profitability for companies operating in the aforementioned segments. The rise in disruptive technology has enabled AI neural networks to adapt and gain information on servicing human clients and has become a critical aspect of the services industry. This analysis will include a breakdown of current and emerging technologies, with a core focus on how hospitality disruptors use and plan to use AI in everyday operations. More specifically, the intern will analyze their application in catering to customer needs and operational workflows, focusing on how geopolitical forces drive innovation in artificial intelligence and beyond. As the Cansbridge Fellowship seeks individuals looking to disrupt and reimagine how technology is used in everyday life, this analysis is essential in reporting how artificial intelligence has and can lead to disruption in the hospitality industry.

Voir la description complète du projet
Superviseur du corps professoral :

Sandy Staples

Étudiant :

Partenaire :

Cansbridge Fellowship

Discipline :

Business

Secteur :

Education; Other services (except public administration)

Université :

Queen's University

Programme :

Business Strategy Internship

Building integrative machine learning framework for precision oncology

Traditional cancer treatments have followed a “one size fits all” approach, which limits efficacy and often results in significant side effects.
This research project aims to develop an approach to predict the impact of cancer missense mutations on the drug-protein interactions of cancer treatments. The approach will use the patient’s own genomic profile and will help to tailor cancer treatments for the patient. This will reduce side effects and costs to the patient by selecting optimal treatment options for individual patients. Using binding affinity as a measure of drug efficacy this research will follow techniques similar to prior works, with the use of graph representation learning for the drugs and targets. This project will also explore several ideas to allow for better results by considering the uniqueness of this problem, such as including information from both wild-type and the mutated protein.
The significance of this work to the partner organization (Princess Margaret Cancer Center) is improved patient care and potential improvements in efficiency/costs with the use of such an automated system.

Voir la description complète du projet
Superviseur du corps professoral :

Arvind Gupta

Étudiant :

Partenaire :

University Health Network

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology

Université :

University of Toronto

Programme :

Accelerate

Développement et validation des méthodes de contrôle non-destructif de pièces métalliques fabriquées par fusion laser sur lit de poudre

Le but de cette recherche est d’établir un protocole de contrôle pour détecter efficacement les défauts présents dans des pièces métalliques produites par une méthode appelée “fabrication additive”. Le contrôle de qualité des pièces produites est une étape cruciale avant leurs mises en service. Différentes techniques de contrôle sont disponibles ayant chacune des avantages et des limitations en termes de précision, de capacité de détection et de coûts associés. Des défauts représentatifs des problèmes qui peuvent survenir lors de la fabrication seront introduits intentionnellement et d’une façon contrôlée dans des pièces pour évaluer les limites de différentes techniques de contrôle de qualité. Un intérêt particulier sera porter à la méthode tomographie par rayons-X, qui permet de voir l’intérieur des pièces sans les endommager se basant sur le même principe que les radiographies médicales. Ce projet permettra au partenaire industriel d’optimiser la qualité et les coûts du contrôle des pièces produites.

Voir la description complète du projet
Superviseur du corps professoral :

Vladimir Brailovski

Étudiant :

Partenaire :

Pratt & Whitney Canada;Pratt & Whitney (US)

Discipline :

Engineering

Secteur :

Manufacturing

Université :

École de technologie supérieure

Programme :

Elevate

Text-to-Image Diffusion Models for Product Image Generation

Ecomtent focuses on developing vertical-specific generative AI models for e-commerce brands, offering a self-service tool to allow customers to generate an unlimited number of high-quality images in any scenario. To this end, we leverage a textto- image model which will be trained to recontextualize any image via a simple text prompt. In particular, we seek to explore additional data-type, beyond just text-prompts and images, that the model can be trained on in order to improve the fidelity of its output. The successful completion of this project will enable Ecomtent to offer their customers a model whose output is realistically recontextualized, yet also very faithful to the original image’s details. This will provide Ecomtent a competitive edge in the content-generation industry.

Voir la description complète du projet
Superviseur du corps professoral :

Kirill Serkh;Sushant Sachdeva

Étudiant :

Partenaire :

Ecomtent

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Design and develop a computer vision system to detect anomalies in the bus stop using the SCiNe device of BusPas

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

BusPas Inc.

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

High frequency measurement generation model from low frequency features

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Voir la description complète du projet
Superviseur du corps professoral :

Aaron Courville

Étudiant :

Partenaire :

Institut de Recherche Hydro-Québec

Discipline :

Computer science

Secteur :

Professional, scientific and technical services; Utilities

Université :

Université de Montréal

Programme :

Accelerate

Deep Transfer Learning for Diagnostics from Eye Fundus Images

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

Optina Diagnostics

Discipline :

Computer science

Secteur :

Manufacturing

Université :

Université de Montréal

Programme :

Accelerate

Quantifying and Combatting the Sexually Dimorphic Risks and Outcomes of Tobacco Dependency in Downtown Ottawa: A Mixed Methods Community-Based Participatory Action Research Project (SDRTT-Ottawa)

Smoking and all other forms of tobacco exposure are known to cause extremely detrimental health effects. It is the leading cause of preventable morbidity and mortality worldwide, and quitting smoking can increase life expectancy by as much 10 years. Tobacco is associated with poor reproductive health outcomes, cardiovascular disease, lung cancer, diabetes, blindness, and much more. It is lesser commonly known that tobacco affects male and female differently. There are chemicals in cigarettes such as bisphenol-As or phthalate esters that can increase the risk of estrogen dependent diseases such as breast or ovarian cancer.
Furthermore, the population that is most at risk for developing tobacco dependency involve those who are underprivileged and underserviced, and struggle with housing and food security. Within the community, male and female are predisposed to different risks in the context of acquiring tobacco dependency, and the associated health outcomes.
Therefore, this study aims to quantify the sexually dimorphic health risks of tobacco through the analysis of previous data, as well as work with community members to identify and ameliorate sex-specific risk factors for those in downtown Ottawa.

Voir la description complète du projet
Superviseur du corps professoral :

Smita Pakhale

Étudiant :

Partenaire :

Ottawa Hospital Research Institute

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Professional, scientific and technical services

Université :

University of Ottawa

Programme :

Accelerate

Multi-regional salary prediction model

The task of predicting salaries for a given job title and seniority in a specific region is challenging due to the complexity of various factors as well as the sensitivity of salary data. It’s hard to get an accurate sense of what people are getting paid in many regions in the world and getting harder as companies are hiring globally.
The objective is to create a machine learning model that considers the relevant features such as cost of living, job demand, education levels, etc., to provide valuable insights for quarterly budget planning or recruitment purposes. The model can help in understanding the regional job market trends and compensation practices, for the customers to make more informed decisions.

Voir la description complète du projet
Superviseur du corps professoral :

Mariano Consens

Étudiant :

Partenaire :

Agentnoon

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

University of Toronto

Programme :

Accelerate

Identifying Causal Risk Factors for Hazardous Driving and Accident Propensity for Safer Fleets and Smart Cities

Road safety affects everyone. In an effort to reduce accidents, we need to understand both the driving behavioural patterns that are predictive of accidents, and the environmental factors involved. In order to analysis risks, at first, collect a rich set of data: including Latitude/Longitude, engine RPM, accelerometer data in the X, Y, and Z plane, ambient temperature, and much more. Then, several derived datasets were created from aggregate customer information which provide metadata about the surrounding environment and pulled in numerous third-party data sources. The research will be focused on processing this data in such a way that useful features representing driving behaviours can be extracted for training machine learning models.

Voir la description complète du projet
Superviseur du corps professoral :

Andrei Badescu

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

Early detection of Septic shocks using vital signs

SpassMed’s ShockRanger is a primary product that utilizes vital signs from patient monitors to provide healthcare providers with meaningful signals for clinical decision-making. SpassMed is seeking one or more methods that can accurately forecast shocks, particularly Septic shock, among patients in ICUs. In addition, SpassMed aims to develop models for effectively classifying patients into their diagnosed diseases, with a specific focus on Sepsis. Sepsis and septic shock have the highest mortality rates in hospitals, and time-sensitive interventions are crucial in making the difference between life and death. Research conducted by Critical Care indicates that between 24.4% to 38.8% of patients die from Sepsis and Septic shock. Failure to predict shocks in a timely manner can prevent hospitals from assigning resources and taking immediate action, which can lead to life-threatening consequences.

Voir la description complète du projet
Superviseur du corps professoral :

Igor Jurisica;Vardan Papyan;Adam Stinchcombe

Étudiant :

Partenaire :

SpassMed

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Research and implementation of dash cam installation and image quality automatic detection based on big data

Geotab‘s dash cams deliver a clear and complete picture of harsh driving events and provide crucial video evidence in the case of collisions and insurance disputes. It is essential to ensure the dashcams work well and capture high quality footage.
The object of this project is to automatically detect dash cam installation faulty and monitor the image quality. We will implement a streaming or batch-basis algorithm to send alerts to fleet managers that a camera needs to be fixed, reinstalled, etc. In addition, we will also quarantine the data coming from the dash cam so that that video data will not be used in subsequent training.
In addition, this project also involves building an ML model deployment and production pipeline steps and associated data pipeline steps to add additional valuable data to the products we provided.

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

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