Innovative Projects Realized

Robust Taxonomy out of Receipt Item Labels

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 entry, but also helps customers better understand their spending patterns, as well as support banks and credit unions in providing individualized recommendations that are aligned with customers’ financial goals. Considering the continuously expanding and often closely related sets of product categories, a hierarchical product taxonomy that leverages the relationship – from general to specific – amongst receipt item categories would power downstream features, including more fine-grained categorization of historical purchases and delivery of more personalized financial insights. To achieve such results, we will implement and evaluate natural language processing and machine learning approaches for hierarchical product categorization.

Convolutional Neural Network for Demand Forecasting

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 need for laborious data manipulation. It will also allow model enhancements to scale across many clients rather than requiring from-scratch data manipulation for each new client. Hence, this project will involve the development of a new machine learning model to predict product demand. The model will be trained using historical sales data. Iterative stages of model architecture and fine-tuning will give rise to the final model. Various enhancements to the model architecture will be explored. The main objective is for the final model to be integrated into a modular demand prediction solution at Deloitte.

Enzyme Immobilization on Surface-Enhanced 3D-Printed Structure

Large field trials as platforms to develop digital tools for intercropping

Evaluation of the OISEAU Application

The project is a program evaluation of the mobile application OISEAU: Agents of Nature, designed by the non-profit organization Morning Star Enterprises. Morning Star has developed individual OISEAU applications for six Calgary Parks locations and the launch is the summer 2013.The application is designed to increase children’s exposure and connection with nature, as well as improve their content knowledge and physical activity. Research consistently shows that children’s physical, cognitive, emotional, and moral development are enhanced by experiences with nature and that interactions with nature aid the development of self-concept, personal identity, and environmental protection. The research will be conducted by Ph.D. student Maxine Crawford, and supervised by Dr. Mark Holder, both of UBC Kelowna.

Augmented Reality as an Assistive Technology for Individuals with Visual Impairments

Safety tests and calorimetric aging investigation of lithium-ion batteries

The influence of working memory processes on the selective retrieval of episodic memory contents

Characterization of Silicon Pixel Detectors for the High-Luminosity Large Hadron Collider

Development of a robust SPR system for the detection of growth factors in an insect-based expression platform

Growth factors are signaling molecules used in cell culture that provide cues to cells. They are one of the key inputs and main consumables in lab growth meat production. Because of this, they are the main driver of production costs. Future Fields is a Canadian biotechnology company that has developed a more cost-effective and scalable platform for producing key growth factors for lab grown meat production; however, analysis of growth factors produced in Future Fields’ system is costly and time consuming. Surface plasmon resonance (SPR) is a novel method of efficient and highly sensitive detection of proteins. The implementation of SPR into Future Fields’ production system would have profound impacts on their ability to produce growth factor products faster and more cost effectively.

Persuasive technology – Guidance to Virtual Relationship Manager (VRM) for effective sales effort basis voice data mining

Voice of the Customer (VoC) is how companies hear and listen to customer feedback about their brand, products, and services. Voice of the Customer solutions convert gathered feedback into valuable data and insights at scale. Data-driven VoC analytics programs are proven to increase customer lifecycle value and lower customer churn. Companies in various industries including insurance, financial services, and healthcare are leveraging this technology to generate insights into customer needs. ICICI Bank has large number for Virtual Relationship Managers who engage with customers assigned to them through electronic channels only for various product sales and servicing. This project’s primary objective is to develop a machine learning model which incorporates both customer record available with the bank along with voice data for finding potential buyers of the products of the bank. We also aim to create a voicebot to make personalized recommendations based on customer history and preferences.

Asset Vision: Extracting Metadata from Engineering Drawing Images

Organizations have traditionally struggled with asset management as a result of not having a complete picture of the location and state of their physical assets. This is largely because critical information such as how an asset was built and should operate is locked in highly technical diagrams and unstructured documents. Asset Vision is a new machine learning solution that automates the asset tag extraction process yielding critical asset information in a more time and cost-effective manner than hiring professional experts. The solution applies techniques from Optical Character Recognition, Natural Language Processing, and Machine Learning to identify, classify, and extract asset information from documents.