Development of AI framework for the prediction of future anomalous events

点击翻译按钮获取中文摘要

This Catalyst grant is aimed at building partnership between the Universities of Toronto and United Arab Emirate (UAE) on developing an artificial intelligence (AI) framework for predicting anomalous events. Anomalous events can severely impact the health, safety and economical situations. These events can be commonly found in many applications, including natural disasters (e.g., earthquakes), biomedical engineering (e.g., seizures), machine fault diagnosis (e.g., malfunction of components) or others (e.g., real estate meltdown). These events not only occur rarely but can be very diverse and infrequent. The generic supervised machine/deep learning approaches may not be directly applied to this problem. The traditional approach to handle this problem is to either 'detect' these events after their occurrence or develop 'forecasting' models that are meant for predicting general future trends. Considering the consequences of anomalous events, we want to develop AI models that can not only predict these events before they occur but also give sufficient lead time to take preventive measures and reduce the aftereffects. Our main hypothesis is that in many situations, a brief time period before an anomalous event (pre-anomalous) can contain important signatures that may differ from rest of the normal data, indicating its onset. Therefore, we are proposing a novel deep learning framework that can leverage either the normal data or combined with pre-anomalous data to detect those changes and predict the occurrence of events of concerns. The Canadian team has access to two large multimodal sensor datasets comprising of various physiological modalities. Along with our UAE collaborator, we have a unique blend of expertise in building classification and anomaly detection models besides signal processing and domain expertise. This project will mobilize both the investigators to visit each other, organize a workshop, brainstorm and meet respective senior leadership to explore future collaborations and funding, publish research papers, and student exchange opportunities. The Canadian PI and one of our graduate students will visit the Big Data Analytics Center at UAE. This project will partially fund two HQPs on the Canadian side.