Computer Sciences Commons^™

Data-Driven Decision Support Tool Co-Development With A Primary Health Care Practice Based Learning Network, Jacqueline K. Kueper, Jennifer Rayner, Sara Bhatti, Kelly Angevaare, Sandra Fitzpatrick, Paulino Lucamba, Eric Sutherland, Daniel J. Lizotte

Epidemiology and Biostatistics Publications

Background: The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This case study describes the ongoing process and lessons learned from the first Alliance for Healthier Communities’ Practice Based Learning Network (PBLN) data-driven decision support tool co-development project.

Methods: We employ an iterative approach to problem identification and methods development for the decision support tool, moving between discussion sessions and case studies with CHC electronic health record (EHR) data. We summarize our work to date in …

Predicting Network Failures With Ai Techniques, Chandrika Saha

Electronic Thesis and Dissertation Repository

Network failure is the unintentional interruption of internet services, resulting in widespread client frustration. It is especially true for time-sensitive services in the healthcare industry, smart grid control, and mobility control, among others. In addition, the COVID-19 pandemic has compelled many businesses to operate remotely, making uninterrupted internet access essential. Moreover, Internet Service Providers (ISPs) lose millions of dollars annually due to network failure, which has a negative impact on their businesses. Currently, redundant network equipment is used as a restoration technique to resolve this issue of network failure. This technique requires a strategy for failure identification and prediction to …

Towards Automated Mineral Identification In Martian Rocks From X-Ray Diffraction Patterns, Luke Tambakis

Electronic Thesis and Dissertation Repository

The CheMin (Chemistry and Mineralogy) instrument on the Curiosity rover has provided a rich set of X-ray diffraction (XRD) patterns from Martian rocks and regolith. These XRD patterns have allowed geologists to make exciting new discoveries about the mineralogy and the geological history of Mars. These discoveries pave the way for further Martian exploration and provide a deeper understanding of Martian geology. The Curiosity rover is very slow by design, travelling at about 4 cm/s. New, faster rovers are being developed to increase scientific throughput and exploration. XRD is valuable for future missions as it can produce new discov- eries …

Inaugural Artificial Intelligence For Public Health Practice (Ai4php) Retreat: Ontario, Canada, Jacqueline K. Kueper, Laura C. Rosella, Richard G. Booth, Brent D. Davis, Sarah Nayani, Maxwell J. Smith, Dan Lizotte

Computer Science Publications

The Artificial Intelligence (AI) for Public Health Practice Retreat was a hybrid event held in October 2022 in London, Ontario to achieve three main goals: 1) Identify both the goals of public health practitioners and the tasks that they undertake as part of their practice to achieve those goals that could be supported by AI, 2) Learn from existing examples and the experience of others about facilitators and barriers to AI for public health, and 3) Support new and strengthen existing connections between public health practitioners and AI researchers. The retreat included a keynote presentation, group brainstorming exercises, breakout group …

Ai Applications On Planetary Rovers, Alexis David Pascual

Electronic Thesis and Dissertation Repository

The rise in the number of robotic missions to space is paving the way for the use of artificial intelligence and machine learning in the autonomy and augmentation of rover operations. For one, more rovers mean more images, and more images mean more data bandwidth required for downlinking as well as more mental bandwidth for analyzing the images. On the other hand, light-weight, low-powered microrover platforms are being developed to accommodate the drive for planetary exploration. As a result of the mass and power constraints, these microrover platforms will not carry typical navigational instruments like a stereocamera or a laser …

Reducing Negative Transfer Of Random Data In Source-Free Unsupervised Domain Adaptation, Anthony Wong

Electronic Thesis and Dissertation Repository

In domain adaptation, a model trained on one dataset (source domain) is applied to a different but related dataset (target domain). The most cutting-edge method is unsupervised source-free domain adaptation (SFDA), in which source data, source labels, and target labels are not available during adaptation. This thesis explores a realistic scenario where the target dataset includes some images that are unrelated to the adaptation process. This scenario can occur from errors in data collection or processing. We provide experiments and analysis to show that current state-of-the-art (SOTA) SFDA methods suffer significant performance drops under a specific domain adaptation setup when …

Machine Learning With Big Data For Electrical Load Forecasting, Alexandra L'Heureux

Electronic Thesis and Dissertation Repository

Today, the amount of data collected is exploding at an unprecedented rate due to developments in Web technologies, social media, mobile and sensing devices and the internet of things (IoT). Data is gathered in every aspect of our lives: from financial information to smart home devices and everything in between. The driving force behind these extensive data collections is the promise of increased knowledge. Therefore, the potential of Big Data relies on our ability to extract value from these massive data sets. Machine learning is central to this quest because of its ability to learn from data and provide data-driven …

The Role Of Transient Vibration Of The Skull On Concussion, Rodrigo Dalvit Carvalho Da Silva

Electronic Thesis and Dissertation Repository

Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to …

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

A Deep Topical N-Gram Model And Topic Discovery On Covid-19 News And Research Manuscripts, Yuan Du

Electronic Thesis and Dissertation Repository

Topic modeling with the latent semantic analysis (LSA), the latent Dirichlet allocation (LDA) and the biterm topic model (BTM) has been successfully implemented and used in many areas, including movie reviews, recommender systems, and text summarization, etc. However, these models may become computationally intensive if tested on a humongous corpus. Considering the wide acceptance of machine learning based on deep neural networks, this research proposes two deep neural network (NN) variants, 2-layer NN and 3-layer NN of the LDA modeling techniques. The primary goal is to deal with problems with a large corpus using manageable computational resources.

This thesis analyze …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Automated Anomaly Detection And Localization System For A Microservices Based Cloud System, Priyanka Prakash Naikade

Electronic Thesis and Dissertation Repository

Context: With an increasing number of applications running on a microservices-based cloud system (such as AWS, GCP, IBM Cloud), it is challenging for the cloud providers to offer uninterrupted services with guaranteed Quality of Service (QoS) factors. Problem Statement: Existing monitoring frameworks often do not detect critical defects among a large volume of issues generated, thus affecting recovery response times and usage of maintenance human resource. Also, manually tracing the root causes of the issues requires a significant amount of time. Objective: The objective of this work is to: (i) detect performance anomalies, in real-time, through monitoring KPIs (Key Performance …

A Hybrid Approach To Procedural Dungeon Generation, Mathias Paul Babin

Electronic Thesis and Dissertation Repository

This thesis presents a novel approach to the Procedural Content Generation (PCG) of both maze and dungeon environments. The solution we propose in this thesis borrows techniques from both Procedural Content Generation via Machine Learning as well as Constructive PCG methods. The approach we take involves decomposing the problem of level generation into a series of stages which begins with the production of macro-level functional structures and ends with micro-level aesthetic details; specifically, we train a Deep Convolutional Neural Network to produce high-quality mazes, which in turn, are transformed into the rooms of larger dungeon levels using a constructive algorithm. …

Machine Learning With Digital Signal Processing For Rapid And Accurate Alignment-Free Genome Analysis: From Methodological Design To A Covid-19 Case Study, Gurjit Singh Randhawa

Electronic Thesis and Dissertation Repository

In the field of bioinformatics, taxonomic classification is the scientific practice of identifying, naming, and grouping of organisms based on their similarities and differences. The problem of taxonomic classification is of immense importance considering that nearly 86% of existing species on Earth and 91% of marine species remain unclassified. Due to the magnitude of the datasets, the need exists for an approach and software tool that is scalable enough to handle large datasets and can be used for rapid sequence comparison and analysis. We propose ML-DSP, a stand-alone alignment-free software tool that uses Machine Learning and Digital Signal Processing to …

Signet: A Neural Network Architecture For Predicting Protein-Protein Interactions, Muhammad S. Ahmed

Electronic Thesis and Dissertation Repository

The study of protein-protein interactions (PPI) is critically important within the field of Molecular Biology, as proteins facilitate key organismal functions including the maintenance of both cellular structure and function. Current experimental methods for elucidating PPIs are greatly hindered by large operating costs, lengthy wait times, as well as low accuracy. The recent development of computational PPI predicting techniques has worked to address many of these issues. Despite this, many of these methods utilize over-engineered features and naive learning algorithms. With the recent advances in Machine Learning and Artificial Intelligence, we attempt to view this problem through a novel, deep …

Machine Learning Of Lifestyle Data For Diabetes, Yan Luo

Electronic Thesis and Dissertation Repository

Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The …

Geological Object Recognition In Extraterrestrial Environments, Gregory M. Elfers

Electronic Thesis and Dissertation Repository

On July 4 1997, the landing of NASA’s Pathnder probe and its rover Sojourner marked the beginning of a new era in space exploration; robots with the ability to move have made up the vanguard of human extraterrestrial exploration ever since. With Sojourners landing, for the rst time, a ground traversing robot was at a distance too far from earth to make direct human control practical. This has given rise to the development of autonomous systems to improve the e?ciency of these robots,in both their ability to move,and their ability to make decisions regarding their environment. Computer Vision comprises a …

Identification Of Informativeness In Text Using Natural Language Stylometry, Rushdi Shams

Electronic Thesis and Dissertation Repository

In this age of information overload, one experiences a rapidly growing over-abundance of written text. To assist with handling this bounty, this plethora of texts is now widely used to develop and optimize statistical natural language processing (NLP) systems. Surprisingly, the use of more fragments of text to train these statistical NLP systems may not necessarily lead to improved performance. We hypothesize that those fragments that help the most with training are those that contain the desired information. Therefore, determining informativeness in text has become a central issue in our view of NLP. Recent developments in this field have spawned …

Deep Learning Via Stacked Sparse Autoencoders For Automated Voxel-Wise Brain Parcellation Based On Functional Connectivity, Céline Gravelines

Electronic Thesis and Dissertation Repository

Functional brain parcellation – the delineation of brain regions based on functional connectivity – is an active research area lacking an ideal subject-specific solution independent of anatomical composition, manual feature engineering, or heavily labelled examples. Deep learning is a cutting-edge area of machine learning on the forefront of current artificial intelligence developments. Specifically, autoencoders are artificial neural networks which can be stacked to form hierarchical sparse deep models from which high-level features are compressed, organized, and extracted, without labelled training data, allowing for unsupervised learning. This thesis presents a novel application of stacked sparse autoencoders to the problem of parcellating …

Automatic Classification Of Epilepsy Lesions, Junwei Sun

Electronic Thesis and Dissertation Repository

Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. Epileptic seizures result from abnormal, excessive or hypersynchronous neuronal activity in the brain. Seizure types are organized firstly according to whether the source of the seizure within the brain is localized or distributed. In this work, our objective is to validate the use of MRI (Magnetic Resonance Imaging) for localizing seizure focus for improved surgical planning. We apply computer vision and machine learning techniques to tackle the problem of epilepsy lesion classification. First datasets of digitized histology images from brain cortexes of different patients are obtained …