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Full-Text Articles in Engineering
Unsupervised Learning For Anomaly Detection In Remote Sensing Imagery, Husam A. Alfergani
Unsupervised Learning For Anomaly Detection In Remote Sensing Imagery, Husam A. Alfergani
Theses and Dissertations
Landfill fire is a potential hazard of waste mismanagement, and could occur both on and below the surface of active and closed sites. Timely identification of temperature anomalies is critical in monitoring and detecting landfill fires, to issue warnings that can help extinguish fires at early stages. The overarching objective of this research is to demonstrate the applicability and advantages of remote sensing data, coupled with machine learning techniques, to identify landfill thermal states that can lead to fire, in the absence of onsite observations. This dissertation proposed unsupervised learning techniques, notably variational auto-encoders (VAEs), to identify temperature anomalies from …
Inverted Cone Convolutional Neural Network For Deboning Mris, Oliver John Palumbo
Inverted Cone Convolutional Neural Network For Deboning Mris, Oliver John Palumbo
Theses and Dissertations
Data plenitude is the power but also the bottleneck for data-driven approaches, including neural networks. In particular, Convolutional Neural Networks (CNNs) require an abundant database of training images to achieve a desired high accuracy. Current techniques employed for boosting small datasets are data augmentation and synthetic data generation, which suffer from computational complexity and imprecision compared to original datasets. In this thesis, we intercalate prior knowledge based on the temporal relation between the images in the third dimension. Specifically, we compute the gradient of subsequent images in the dataset to remove extraneous information and highlight subtle variations between the images. …