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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 ...
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 ...
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 ...