Master of Science, Computer Science
Research
Scrawl is engaged in significant research and development projects in collaboration with Mitacs and our research partner institutions to advance innovative research for both critical Scrawlr capabilities and broad application.
Image Content Analysis
We are utilizing the latest machine-learning technologies as a starting point for text extraction and object identification within images, with an emphasis on enhanced performance and accuracy.
In-memory Database Optimization
Leveraging machine-learning technology for in-memory database performance optimization. We are creating new data management strategies to facilitate loading and off-loading data to meet expected demand at scale.
Text and Sentiment Similarity at Scale
Utilizing lexicographic and sentiment analysis, we are developing methods to perform text-based sentiment comparison on large-scale datasets while achieving performance objectives. Our research focus is on a combination of search-space reduction, CPU and GPU core optimization, and improved vertical scaling.
Linguistic Content Analysis
We are focused on the highly accurate identification, classification, and intensity assessment of different forms of speech across multiple languages to analyze user driven relevance. We are utilizing leading-edge machine-learning models to facilitate multilingual identification, classification, and assessment systems. This approach will include partnerships with other businesses focused on specific natural language processing approaches to determining intent and meaning within both narrow domains (ie: certain components of medicine) and broad domains.
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