Achieving Clinical Automation in Paediatric Emergency Medicine with Machine Learning Medical Directives

Patients in the US and Canada have been suffering from overcrowding and long wait times in emergency departments, along with poor health conditions. In order to provide guidelines for nurses, medical directives can request certain diagnostic tests during triage, which speeds up the process by providing test results to the physician when making the initial assessment. Our research will use the data from patients’ electronic health records to build and validate various machine learning models and to predict the downstream testing needs for children.

An Improved Approach to Watershed Management and Adaptive Decision Making in the Great Lakes

With collaboration between the Council of the Great Lakes Region, Pollution Probe and Lambton College, the proposed project is focused on continuing the development of an artificial intelligence visualization tool to enable users to select growth constraints and visualize resulting changes to watershed health, predict how watersheds will evolve over time and prescribe actions to protect them. This AI tool’s purpose is to analyze historical watershed data and to predict changes to a watershed over time. Looking into water quality and the minimum and maximum of water quality thresholds.

Anomaly Detection in Highly Noisy Signals from Electrical Rotating Machines

Equipment failure is the primary source of unplanned downtime in industries working with rotating electrical machines. Fault detection at the early stages is an essential solution for reducing this downtime. Condition monitoring of machinery is the process of capturing and monitoring parameters such as vibrations to identify a developing fault. This project uses the data resulting from condition monitoring to develop anomaly detection algorithms for improving early-stage fault detection and diagnosis processes.

AI Shading – An energy efficient Smart Blind technology

Increased energy consumption across the world for heating and cooling indoor living spaces has been a major contributor to global greenhouse gas production. As per 2015 statistics, buildings account for 76% of global electricity consumption and approx. 35% of that energy consumption is for air conditioning, heating, and ventilation. To combat climate change it is imperative to reduce our power consumption.

Decision making with ontologies and measures of diagnosticity

Mineral exploration and natural disaster risk reduction involve reasoning with uncertainty about complex descriptions of parts of the Earth. Such complex reasoning is traditionally carried out by human experts who, through years of training and field experience, develop specific knowledge and are able to take the right decision at the right time. This project will advance the previous work to build computer models and tools that assist human in these decision making processes.

Interactive Visual Analysis of Calcium Imaging Data

With the advances of microscopy techniques, scientists can monitor the neurons in large brain areas of living animals. But at the same time, it creates significant challenges for scientists to make sense of the generated data due to its scale and complexity. This calls for new componential methods and systems for augmenting the current workflows of neuroscientists. This research aims to address this problem by leveraging and innovating advanced machine learning and visualization techniques.

Optimizing High Performance Distributed Computing Framework in Heterogeneous Environment System at Lakehead University

Universities in Canada and around the world are adopting the DCP (Distributed Compute Protocol) as a method of obtaining free, abundant compute resources for research and innovation. In doing so, IT departments are deploying DCP workers on fleets of desktop computers in departments, libraries and administration offices on campuses. All of these computers, once connected to the distributed computer, consume network bandwidth, switching, and power resources. DCP is unique. Other utilities such as networks, cloud compute, and/or other mainframe systems have existed for years.

4G Cellular Trespasser Detection System for Farms

There is a strong demand for effective means to detect and record trespass activities onto farm properties, which cost the agriculture industry many hundreds of millions of dollars each year. The goal of the project is to design and develop a trespasser detection system based on the presence of a human being carrying a cell phone. The detection system can be used to surveil farm premises in order to detect and immediately report the presence of any person illegally entering/trespassing on agricultural properties.

Parallel multibody solver coupling algorithms

This project concerns the efficient simulation of constrained-multi body systems with applications in training simulations. For instance, a crane on a construction site can be modeled and simulated as a collection of rigid bodies connected by rotational joints. Simulation of contact and friction is similar but a challenge because the force is bounded (i.e., forces are not allowed to act like glue and can only push objects apart). When there are large numbers of bodies in a simulation, with many frictional contacts, these systems can be challenging to
solve.

A Predictive Cluster-based Machine Learning Pricing Model

Dynamic pricing models create price by assessing total cost, demand, and timing to customize the price to the moment. The models enable both buyers and sellers to settle a price that is very custom to their specific needs. Bison Transport Inc. has a network model that monitors profit and a pricing engine that monitors margin. The network model needs to evolve in critical ways to facilitate dynamic pricing. The current model allows viewing of the network from a variety of vantage points- region, customer, driver, asset, service type and time (day of week, time of day, season of year).

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