Now showing items 1-6 of 6
Exploring Machine Learning Approaches to Precipitation Prediction: Post Processing of Daily Accumulated North American forecasts
(University of Winnipeg, 2023-11-22)
This thesis presents recent work on exploring machine learning (ML) and deep learning (DL) models to improve the accuracy of 24 hour precipitation forecasts. Leveraging a comprehensive North American dataset of precipitation ...
Using Machine Learning to Improve Neutron Tagging Efficiency in Water Cherenkov Detectors
(University of Winnipeg, 2021-11-25)
When an anti-neutrino collides with a proton in the atomic nucleus, it yields an anti-lepton and a free neutron. In a water Cherenkov neutrino detector like Super-K or the next generation Hyper-K, the free neutron is ...
Securing Intrusion Detection Systems in IoT Networks Against Adversarial Learning: A Moving Target Defense Approach based on Reinforcement Learning
(University of Winnipeg, 2023-08-23)
Investigating the use of moving target defense (MTD) mechanisms in IoT networks is ongoing research, with unfathomable potential to equip IoT devices and networks with the ability to fend off cyber attacks despite the ...
Rough-set based learning methods: A case study to assess the relationship between the clinical delivery of cannabinoid medicine for anxiety, depression, sleep, patterns and predictability
(University of Winnipeg, 2022-08-22)
COVID-19 is an unprecedented health crisis causing a great deal of stress and mental health challenges in populations in Canada. Recently, research is emerging highlighting the potential of cannabinoids’ beneficial effects ...
Short Text Classification with Tolerance Near Sets
(University of Winnipeg, 2021-08-13)
Text classification is a classical machine learning application in Natural Language Processing, which aims to assign labels to textual units such as documents, sentences, paragraphs, and queries. Applications of text ...
Securing Federated Learning Model Aggregation Against Poisoning Attacks via Credit-Based Client Selection
(University of Winnipeg, 2023-08-29)
Federated Learning (FL) has emerged as a revolutionary paradigm in the field of machine learning, enabling multiple participants to collaboratively train models without compromising the privacy of their individual training ...