This proposal aims to develop a robust, low complexity infant cry deep learning classification based on various babies’ responses to physiological needs such as hunger or to discomfort and pain. The significance of this research lies in its potential to enhance early detection of needs and moods in newborns, contributing to improved infant care, early […]

The continued health disparities and the recent public health challenges including COVID-19 pandemic and epidemics have highlighted for the need of more resilient community-based health system in Africa and beyond. Robust evidence is crucial by examining the lessons learned during practice of community-based health programs to build locally led better community health systems in Africa. […]

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The general objective of this research is to investigate the effectiveness of fine-tuning large language models (LLMs) for the purpose of enhancing patent generation and brainstorming processes across various domains. The project follows an agile project management approach, emphasizing continuous small releases. The project is important to XLSCOUT as it aims to enhance text clustering, […]

Software requirements play a critical role in safety-critical cyber-physical systems as they are the foundation for designing, implementing, and testing of the software systems. Due to limited budget and time constraints, software requirements in a commercial setting tend to have unsatisfactory quality, which complicates downstream development activities. Prior research identifies dozens of quality attributes of […]

Cardiac PET is a powerful tool to assess heart function. One measure from a Cardiac PET study is left ventricle ejection fraction. This is mainly estimated through gated reconstructions of relative perfusion imaging and segmentation of the left ventricle myocardium. However, direct estimations of left ventricle volume and thus ejection fraction can be obtained by […]

Training artificial intelligence and machine learning (AIML) models requires a large amount of data. However, access to data for AIML projects has been problematic in practice, largely due to privacy concerns. One approach to enable data sharing and data access is to anonymize the data. However, in the Canadian context, a national standard and a […]

The project focuses on transforming clinical documentation with advanced AI, specifically Large Language Models (LLMs), to alleviate the burden on healthcare professionals. Aimed at improving efficiency and accuracy, the strategy involves creating an AI-powered charting assistant that seamlessly turns clinician-patient interactions into standardized clinical documents, offered as a subscription-based SaaS platform. Initiatives include fine-tuning LLMs […]

Agriculture and Agri-Food Canada has initiated a program to reduce fertilizer N2O emissions by 30 % from 2020 to 2030 [14]. Accurate monitoring of agricultural N2O emissions is crucial for understanding their environmental impact and implementing effective mitigation strategies. Current research mainly focuses on measuring N2O emissions from the ground. The spectroscopic-based LI-COR sensor is […]

The goal of this project is to use advanced technology, specifically a type of Artificial Intelligence (AI), to process educational content. This allows us to create personalized learning experiences for students in a secure setting. Over time, this unique learning tool will become an essential resource, encouraging students to explore and gain knowledge on specialized […]

Apption is an established data and software services provider, with a track-record of sucessful product incubation and commercialization. Apption is planning to reinvest in marketing initiatives, hoping to drive growth of our professional services in the data and AI space as well as our related AI-product offering: Datahunter. Datahunter automates data cataloguing and management tasks […]

This project will recommend strategies for deploying and scaling a training program to support commercialization skills for biomedical researchers.