Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"Autoencoder models"”.
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On the Effectiveness of Dimensionality Reduction for Unsupervised Structural Health Monitoring Anomaly Detection
… various DR techniques, including neural autoencoder models, to capture the impact on two SHM benchmark datasets exclusively. Results imply the loss of information to be more detrimental, reducing the novelty detection accuracy by up to 60\% with autoencoder-based DR. Regularization can …
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Using deep learning to characterise weak signals in global equity markets: a case study of COVID-19
… fits of GJR-GARCH and deep-undercomplete-autoencoder models are deployed. Resultantly, measures of dispersion and atypicality are produced which allow for effective and clear characterisation of the degree of typicality of the equity prices and their movements. This innovative method …
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Intrusion Detection Systems using Machine Learning and Deep Learning techniques
… This thesis further investigates the use of autoencoders to detect zero-day attacks.<br/>The zero-day attack detection experiments highlight the problem of discriminating benign-mimicking attacks. To overcome this challenge, an additional layer of feature abstraction is proposed; to improve …
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Autoencoder-based techniques for improved classification in settings with high dimensional and small sized data
Neural network models have been widely tested and analysed usinglarge sized high dimensional datasets. In real world application prob-lems, the available datasets are often limited in size due to reasonsrelated to the cost or difficulties encountered while collecting the data.This limitation in the …
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Learning and evaluating image representations
… and removing rain, cracks, and glare, autoencoder models are learned from fake data and then shown to be applicable on real images. For learning to decompose rainy images cycle consistency losses are incorporated to learn without examples of de-rained images. In Face-to-Face …
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The role of representations in human activity recognition
… we develop convolutional and recurrent autoencoder architectures for feature learning and compare their performance to a distribution-based representation as well as a supervised deep learning representation based on the DeepConvLSTM architecture. This is motivated by the promises deep …
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Inferential measurement for integrity and security of cyber-physical systems.
… and data-driven approaches. Data-driven models generally outperform model-driven ones by leveraging data for decision-making. However, they struggle to capture the complete spatiotemporal relationships in time series sensor data, which can lead to degraded performance. When developing a …