SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted Towards Cost-Effective Noise-Resilient Machine Learning Solutions(University of Georgia, 2026-06-04) Gharawi, Abdulrahman Ahmed; Ramaswamy, LakshmishMachine learning models have demonstrated exceptional performance in various applications as a result of the emergence of large labeled datasets. Although there are many available datasets, acquiring high-quality labeled datasets is challenging since it involves huge human supervision or expert annotation, which are extremely labor-intensive and time-consuming. The problem is magnified by the considerable amount of label noise present in datasets from real-world scenarios, which significantly undermines the performance accuracy of machine learning models. Since noisy datasets can affect the performance of machine learning models, acquiring high-quality datasets without label noise becomes a critical problem. However, it is challenging to significantly decrease label noise in real-world datasets without hiring expensive expert annotators. Based on extensive testing and research, this dissertation examines the impact of different levels of label noise on the accuracy of machine learning models. It also investigates ways to cut labeling expenses without sacrificing required accuracy. Finally, to enhance the robustness of machine learning models and mitigate the pervasive issue of label noise, we present a novel, cost-effective approach called Self Enhanced Supervised Training (SEST).21 0Item Restricted ENSEMBLE MACHINE LEARNING IN SPACE WEATHER ANALYTICS(New Jersey Institute of Technology, 2024) Alobaid, Khalid; Wang, JasonThis dissertation addresses several important space weather problems using ensemble learning techniques. An ensemble method combining multiple machine learning models is often more accurate than the individual machine learning models that form the ensemble method. There are several techniques for constructing an ensemble. With in-depth case studies, the dissertation demonstrates the usefulness and effectiveness of ensemble machine learning for space weather analytics, especially for predicting extreme space weather events such as coronal mass ejections (CMEs). The dissertation begins with an ensemble method for predicting the arrival time of CMEs from the Sun to Earth. The proposed method, named CMETNet, combines classical machine learning algorithms such as support vector regression, random forests, XGBoost and Gaussian process regression, along with a deep convolutional neural network (CNN), to perform multimodal learning. The classical machine learning algorithms are used to learn latent patterns from CME features and background solar wind parameters while the deep CNN is used to learn patterns hidden in CME images where the learned patterns are jointly used to make predictions. Experimental results show that CMETNet outperforms existing models, both machine learning based and physics based. Finally, the dissertation presents a fusion method, named DeepCME, to estimate two important properties of CMEs, namely, CME mass and kinetic energy. The DeepCME method is a fusion of three deep-learning models, namely ResNet, InceptionNet, and InceptionResNet. The fusion model extracts features from Large Angle and Spectrometric Coronagraph (LASCO) C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. To the best of current knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations. DeepCME can help scientists better understand CME dynamics. In conclusion, the dissertation showcases many applications of learning techniques including ensemble learning, deep learning, transfer learning and multimodal learning in space weather analytics. The tools and methods developed from the dissertation will make contributions to the understanding and forecasting of CME dynamics and CME geoeffectiveness.16 0