Advancing video categorization
Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble is collaborating with Dr. Vida Movahedi from Seneca’s School of Information and Communication Technology to develop a machine-learning algorithm that will automatically output highly probable categories for videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This video identification research project will be Vubble’s first step in understanding how to automate the identification of accurate video. The need for automation of videos curation is prevalent, as video is quickly becoming the world’s dominant form of media consumption, particularly for digital native younger audiences. Furthermore, the results of the applied research will aid Vubble in moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake news video created from archival footage.
Machine learning and blockchain-backed optimized assignment matching for PSWs to improve understaffing and risk during the COVID-19 outbreak
TriNetra Systems Inc. (TriNetra) is an agile and fast-growing enterprise software development and services company specializing in IT-business alignment for service-oriented architectures, with clients like e-Health Ontario. TriNetra is developing blockchain tools and solutions to aid in establishing a system of trust and transparency in professional healthcare market enterprise architecture, DevOps, cloud and mobile technologies, IT processes, and governance. Their project called “Octochain” uses blockchain technology for an online, fast, easy and reliable way to confirm credentials. TriNetra is collaborating with ConnexHealth, a Personal Support Worker (PSW) placement company, to implement a system for verifying PSW candidate qualifications, achievements, certifications and résumés. TriNetra is proposing to collaborate with Seneca to extend the features of this new blockchain system to include machine learning/artificial intelligence (ML/AI) capabilities to match candidates to job assignments based on their entire profile, including certifications, training, geography, work history, and availability. It increases trust between the PSWs and employers, and thus improves social assistance and medical service planning for elderly and disabled individuals. It empowers PSWs by providing suitable assignments and by improving pay, because of validated credentials and experience. Responding to the vastly changed PSW market and constraints caused by the COVID-19 pandemic, this project will empower, efficiently mobilize and sustainably deploy individual PSWs.
Machine learning and blockchain-backed optimized assignment matching for PSWs to improve understaffing and risk during the COVID-19 outbreak
TriNetra Systems Inc. (TriNetra) is an agile and fast-growing enterprise software development and services company specializing in IT-business alignment for service-oriented architectures, with clients like e-Health Ontario. TriNetra is developing blockchain tools and solutions to aid in establishing a system of trust and transparency in professional healthcare market enterprise architecture, DevOps, cloud and mobile technologies, IT processes, and governance. Their project called “Octochain” uses blockchain technology for an online, fast, easy and reliable way to confirm credentials. TriNetra is collaborating with ConnexHealth, a Personal Support Worker (PSW) placement company, to implement a system for verifying PSW candidate qualifications, achievements, certifications and résumés. TriNetra is proposing to collaborate with Seneca to extend the features of this new blockchain system to include machine learning/artificial intelligence (ML/AI) capabilities to match candidates to job assignments based on their entire profile, including certifications, training, geography, work history, and availability. It increases trust between the PSWs and employers, and thus improves social assistance and medical service planning for elderly and disabled individuals. It empowers PSWs by providing suitable assignments and by improving pay, because of validated credentials and experience. Responding to the vastly changed PSW market and constraints caused by the COVID-19 pandemic, this project will empower, efficiently mobilize and sustainably deploy individual PSWs.