Advanced Analytics in Multiple Sclerosis Research

The multiple sclerosis (MS) clinic at St. Michael’s Hospital (SMH) is among the largest in the world. While considerable data is collected from the MS clinic in both structured and unstructured form, the ability to glean this information to assess quality of care and conduct advanced analytics such as predictive modeling is limited. In this […]

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Intelligent Character Recognition (ICR), Optical Character Recognition (OCR) and machine learning based corrections of data transcription from scanned business documents

SS&C processes more than 80% of financial scanned and faxed documents in the US and requires large amount of manual labor in order to map information from a document into another form. Advances in neural networks applied to computer vision have produced text detection and recognition that nears human performance. This project will be leveraging […]

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Detecting Company-Specific Purchase Evidence from Twitter Posts

Delphia’s business model revolves around using proprietary data sets and data extraction techniques to inform its active trading strategies on the financial markets.  It has been shown that detecting when Twitter users post about recent or future purchases has the potential to increase the accuracy of company sales forecasts, which in turn can inform stock […]

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Multi-morbidity Characterization and Polypharmacy Side Effect Detection for designing Optimal Personalized Healthcare with Machine Learning

Despite a significant improvement in healthcare systems over the past decades, the rapid growth in the number of patients with multiple chronic diseases – called multimorbidity – stands as a complex challenge to healthcare services that are primarily designed to treat individuals with single conditions. Advances in machine learning as well as in computing power […]

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Applying Machine Learning to Predict Demand Transference

The project will help us design a machine learning model that can determine the demand transference of our customers. The key objective of this project is to design, research, build, and experiment with machine learning models to ensure low product waste and high customer satisfaction. The model will have several impactful applications across the organization.

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Multi-Channel User Linkage through Probabilistic Matching

People utilize multiple devices to complete various tasks, making their online identities fragmented. Advertising is as much about knowing when not to promote a product as it is about when to do it. For example, before being sent alcohol and cannabis ads, the user must be identifiable as being over 19. Age information may only […]

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Extending artificial intelligence in the operating room

Assessment of surgical data from an operating room is a complex process that may require significant resources such as expert input and advanced technology. Automation brings a considerable opportunity to greatly reducing these significant resource requirements – e.g., using computer vision software to detect clinically relevant actions during surgery. However, those detections should be interpretable, […]

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Development of the Parasocial Identifier for Social Media

Since the introduction of radio and television, fans have had a connection to those they know in the media. These connections are called “parasocial relationships” and they are one-sided relationships, usually between a fan and a celebrity, that is usually not face-to-face. With the advent of social media, any person, or company, can become a […]

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Enhancements to Smart Disease and Pest Prediction System Through the Use of Machine Learning Techniques

Given the current global environmental crisis, developing sustainable solutions to enhance or replace our current agricultural practices is critical: the agricultural sector exerts important environmental pressure through its aggressive land, water and pesticide usage combined with the ever increasing demand on food supply. Mitigating this problem requires developing more sustainable and efficient agricultural techniques. Precisely, […]

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Characterization of KPI Outliers from Logs Using Data Mining

Ubisoft records the interaction between its customers and its servers in large execution logs (also called traces). Any failure of the system is thus recorded therein. However, the considerable size of these logs considerably hinders their effective use by analysts and developers. We propose an automated method to detect failing executions, and furthermore to characterize […]

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WP 3.2.2 – Automated Log Analysis

Ciena is a Canadian company leader in engineering and manufacturing networking systems and devices. The company has around 5,000 operable products in its portfolio. The vast majority of Ciena products generate logs during the boot up and the mission mode operations from the various tasks running on their real time operating systems. The company wants […]

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