Many retailers are interested in forecasting demand for the products they sell. Deloitte has used machine learning methods to tackle this problem in the past. However, this requires the creation of hand-crafted features based on product sales data, which is a costly and time-intensive process. Using alternative models to perform this task would remove the […]

The primary objective of this project is to implement a product taxonomy model that can reliably categorize receipt item labels to generate more personalized financial insights. Sensibill leverages unstructured receipt data to support personal and business finance management. Reliable categorization of receipt line items into specific merchant categories not only reduces the time for manual […]

With collaboration between the Council of the Great Lakes Region, Pollution Probe and Lambton College, the proposed project is focused on continuing the development of an artificial intelligence visualization tool to enable users to select growth constraints and visualize resulting changes to watershed health, predict how watersheds will evolve over time and prescribe actions to […]

Sensibill provides financial tools like digital receipt data that help banks and credit unions better know and serve their customers. Users can upload digital images through tools and the company would do image processing first and then use processed images to analyze. However, the previous image processing algorithm is time-consuming for users and doesn’t satisfy […]

While taking foreign language tests, people may record responses with different background noises. The contaminated audios can lead to unusual results in speech recognition and scoring by the scoring systems. Pearson would like to develop a more robust system for the automated speech recognition machine to work with clean and noisy records. Audio files are […]

One assumption that is commonly used in machine learning is that samples are statistically independent. In effect, each sample of data doesn’t tell you anything about any other sample in the dataset. This is not true for all types of data; there are some types of datasets where relationships between samples can be modeled as […]

Road safety affects everyone, and companies are looking for ways to identify the risk factors for their fleet drivers, and to reduce the chance of accidents. This project will build on Geotab’s existing methods for assessing driving risks, and develop new techniques to better identify risky drivers and risky behaviours. The project will focus on […]

Companies spend a large amount of money and time on mobile application development which requires knowledge of various native platform programming languages and the different characteristics of these platforms. However, demands for mobile applications are increasing and are becoming difficult to follow for the IT department. One solution seems to be no-code development platforms that […]

Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. In this internship, we analyze a real-world big data set(s) to make sensible inferences by […]

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 a minor collision detection system. On top of driving data and environmental data, it also contains machine diagnostic […]

Over the past few years, the abilities and performances of deep-learning natural language processing (NLP) have evolved dramatically. A main reason for those improvements is the scaling of the number of parameters in models to tens or hundreds of billions. However, it also becomes impractical to deploy those models in production as the computation cost […]

Machine learning has great potential in detecting cognitive, mental and functional health disorders from speech, as acoustic properties of speech and corresponding patterns in language are modified by a variety of health-related effects. Specifically, neural language models, have recently demonstrated impressive abilities in tasks involving linguistic knowledge. Their success in language understanding and classification tasks […]