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Now showing items 51-60 of 62
Application of machine learning algorithm on binary classification model for stroke treatment eligibility
(2023-04-17)
In Canada, stroke is the leading cause of adult disability and the third leading cause of death. Ischemic stroke is the most common type, making up approximately 85% of all stroke patients. Endovascular treatment (EVT) is ...
Exploration of NLP-Based Feature Extraction Techniques for Security Analysis and Anomaly Detection of Service Logs
(2023-04-28)
The goal of this research is to provide security and machine learning (ML) practitioners with deeper insight when selecting features and algorithms for unsupervised log analysis. This thesis explores the effect of traditional ...
Improving Efficacy and Efficiency of Hypothesis Adaptation and Federated Learning
(2023-07-28)
Small computing devices, such as smartphones, constantly collect and store data that can potentially help machines learn complex tasks. However, the data contained on a single device is relatively small and biased, making ...
Evaluation of DeepLabCut as a Human Markerless Motion Capture Tool
(2023-08-31)
There are a variety of motion capture methods available; however, many of them are not well suited for collections outside a laboratory setting. AI markerless motion capture may fit this need, but its implementation and ...
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 ...
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 ...
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 ...
Dispatch Policies between Hubs in the Physical Internet with Emission Considerations and Local Dispatch Using Machine Learning
(2023-12-15)
This thesis presents a comprehensive analysis of dispatch management in Physical Internet (PI) logistics systems. As a modular approach to freight deliveries and container transportation, it aims to reduce costs and ...
CONTEXT-AWARE SEMANTIC TEXT MINING AND REPRESENTATION LEARNING FOR TEXT DISAMBIGUATION AND ONLINE HARASSMENT CLASSIFICATION
(2023-12-15)
This dissertation presents a new method for text representation learning and applies it to two Natural Language Processing (NLP) problems, namely, word sense disambiguation and text classification. Word Sense Disambiguation ...
Applications of Deep Convolutional Neural Networks to Passive Acoustic Monitoring of Baleen Whales
(2024-04-14)
Research into automated detection and classification systems (DCS) of marine mammal vocalizations in acoustic recordings is expanding internationally due to the necessity to analyze large collections of data for conservation ...