Browsing Faculty of Graduate Studies Online Theses by Subject "Machine Learning"
Now showing items 41-60 of 65
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A Machine Learning Approach to Forecasting Consumer Food Prices
(2017-08-24)Building on the success of the Canada Food Price Report 2017 and its inclusion of a machine learning methodology, this research thesis posed and attempted to answer the following question, “What is the best way to predict ... -
A Machine Learning Framework for Host Based Intrusion Detection using System Call Abstraction
(2020-04-13)The number of cyber threats is increasing faster than the number of defensive strategies deployed to tackle those threats. An automated Intrusion Detection System (IDS) has the capability to detect, classify, and predict ... -
ME-IDS: An Ensemble Transfer Learning Framework Based on Misclassified Samples for Intrusion Detection Systems
(2023-12-01)In our digitally interconnected world, the demand for robust security measures has become increasingly apparent, given the escalating threat of cyberattacks on the Internet. Intrusion Detection Systems (IDS) have emerged ... -
Measurement of Heterogeneity in Computational Psychiatry
(2020-04-14)We introduce representational Rényi heterogeneity (RRH), which generalizes existing heterogeneity measurement approaches from ecology (biodiversity measures) and economics (inequality measures). We show that RRH retains ... -
Modeling Activity Selection and Scheduling Behavior of Population Cohorts within an Activity-Based Travel Demand Model System
(2018-04-02)Understanding the time-use activity patterns of population cohorts in the region will contribute greatly to modeling spatio-temporal urban transportation demand models. The research detailed in this dissertation focuses ... -
Modelling Human Target Reaching using A novel predictive deep reinforcement learning technique
(2018-04-03)It is hypothesized that the brain builds an internal representation of the world and its body. Moreover, it is well established that human decision making and instrumental control uses multiple systems, some which are ... -
Multi-Modal Consensus Clustering to Identify Phenotypes of Kidney Transplant Donors and Recipients and Their Association With Survival
(2022-11-18)Kidney transplantation is an essential treatment option for individuals diagnosed with End-stage renal disease (ESRD). Being able to predict the survival of the transplant and the outcome of the recipient is an important ... -
Music Composer Recognition from MIDI Representation using Deep Learning and N-gram Based Methods
(2022-10-07)In order to answer conceptually basic queries like “Who created this piece?” the discipline of computational musicology frequently requires the analysis of detailed characteristics. Melodic lines, rhythmic patterns, ... -
Novel Approaches to Marker Gene Representation Learning Using Trained Tokenizers and Jointly Trained Transformer Models
(2021-08-19)Next-generation DNA sequencing technologies have made marker-gene DNA sequence data widely available. Analysis of microbiome data has many challenges, including sparsity, high cardinality, and intra-study dependencies ... -
On Sustainable Supply Chains: Optimal Design of a Multimodal Logistics Network with Shipment Consolidation, Stochastic Demand, and Machine Learning
(2022-08-15)In this thesis, we consider the logistics network of a multi-echelon multimodal supply chain with multiple products and components taking economic and environmental sustainability, and shipment consolidation into consideration. ... -
Ordinal Variable Imputation for Health Survey Data: A Comparison between Machine Learning and non-Machine Learning Methods
(2021-08-31)Introduction: Large amounts of data are available for analyses from survey datasets. However, missing data can potentially reduce statistical power and/or introduce bias into analyses when not addressed correctly. Data ... -
Predictive modeling of damage and repair for disease and activity of daily living status in ELSA dataset using machine learning models
(2023-12-08)A good predictive model is useful in health sciences for predicting onset of disease, as well as damage or repair of health deficits. One can predict one or more of these quantities depending on the nature of the collected ... -
A PRELIMINARY STUDY FOR IDENTIFYING NAT TRAFFIC USING MACHINE LEARNING
(2014-04-07)It is shown in the literature that the NAT devices have become a convenient way to hide the identity of malicious behaviors. In this thesis, the aim is to identify the presence of the NAT devices in the network traffic and ... -
Reinforcement Learning with Real Valued Tangled Program Graphs
(2021-08-27)Tangled Program Graphs (TPG) represents a framework for evolving programs under an explicitly emergent model for modularity. The framework has been very successful at discovering solutions to tasks with delayed rewards ... -
Studying Oil Spill Transport by Exploring Oil-Mineral-Aggregates Characteristics and Tidal Dispersion Properties
(2023-04-27)Oil spill has been widely recognized as a major marine environmental issue, that could cause a profound and long-term impact on the environment, ecology, and socioeconomics. In particular, when the oil spill happens in ... -
A Symbiotic Bid-Based Framework for Problem Decomposition using Genetic Programming
(2011-03-08)This thesis investigates the use of symbiosis as an evolutionary metaphor for problem decomposition using Genetic Programming. It begins by drawing a connection between lateral problem decomposition, in which peers with ... -
Towards a Label-Free and Representation-Based Metric for Evaluating Machine Learning Models
(2022-08-04)In this work, we explore the viability of proposed label-free metrics to evaluate models. We begin by examining the effect on linear probe accuracy which different viable label schemes on an identical dataset may cause. ... -
TOWARDS IN-NETWORK IMAGE CLASSIFICATION FOR LATENCY-CRITICAL IOT APPLICATIONS
(2021-12-13)As the demand for latency-critical applications (intelligent transport systems, medical imaging, surveillance, AR/VR) continues to increase, tasks such as computer vision, which are imperative to these applications, require ... -
A TRANSFORMER-BASED GRAPH NEURAL NETWORK AGGREGATION FRAMEWORK FOR 5G RADIO LINK FAILURE PREDICTION
(2023-08-31)The prediction of Radio Link Failures (RLF) in Radio Access Networks (RANs) is crucial to ensure smooth communication and meet the demanding requirements of high data rates, low latency, and improved performance in 5G ... -
USING ENSEMBLE CLUSTERING TO IDENTIFY PHENOTYPES OF DIABETES PATIENTS FOR EVALUATING DISEASE PROGRESSION
(2022-04-05)Diabetes Mellitus (DM) is a chronic health condition that affects multiple organs and is associated with significant morbidity and mortality. The management of diabetes requires periodic pathology investigations and physician ...