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 20 of 167 for “"Deep learning methods"”.
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Deep learning methods for large-scale physics
… the 21st century has generated the need for new methods and approaches to mathematical modeling. Machine learning leads the forefront of this change but still requires theoretical frameworks to manage large-scale data and increase the usefulness of models, especially when using deep learning. The …
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Enhancing genomic data quality through deep learning methods
… Building further, I developed GI-Joe, a novel deep learning architecture augmented with transformer blocks and convolutional blocks for scalable genotype imputation. GI-Joe extended the context window to over one million variants, markedly improving accuracy for rare and structural variants …
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Deep Learning Methods for Built Environment Operational Management
… reliable, and scalable time series (TS) deep learning (DL) frameworks toward enhancing operational management in the built environment, with case studies on (i) reliable infrastructure anomaly detection (AD) and (ii) scalable energy forecasting. An unsupervised, univariate probabilistic …
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Democratizing Deep Learning methods by means of AutoML tools
… de aprendizaje profundo, o en inglés \emph{Deep Learning}, se han convertido en el estado del arte para lidiar con problemas de Visión por Computador en casi cualquier ámbito. Este crecimiento se debe a la gran cantidad de imágenes que se capturan diariamente, el incremento de la capacidad …
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Data-Driven Deep Learning Methods for Physically-Based Simulations
In this doctoral thesis, we study and analyze Deep Learning applications to learn physically-based simulations’ results. The first type of application is focused on underground flow analysis problems modeled through Discrete Fracture Networks, training Deep Learning models as reduced models for …
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Forecasting and Modelling Space Weather with Deep Learning Methods
… referred to as space weather. The advent of deep learning has unlocked the ability to use large datasets to model and forecast these conditions. This thesis principally describes a set of methodological improvements, considerations and proof of concept systems that use extreme ultra-violet …
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A Study of Interference Suppression Using Deep Learning Methods
This thesis investigates a Deep Learning model for interference suppression in wireless communications. By exploiting the structure of Convolutional Neural Network-based autoencoders, we develop an approach for interference suppression with no prior knowledge on characteristics or the exact …
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Exploring Deep Learning Methods for Discovering Features in Speech Signals
… to the area of speech recognition with Deep Neural Network - Hidden Markov Models (DNN-HMMs). Firstly, we explore the effectiveness of features learnt from speech databases using Deep Learning for speech recognition. This contrasts with prior works that have largely confined themselves …
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Person ReID in Different Environment Settings Using Deep Learning Methods
… Specifically, this thesis proposes a series of methods for environment change person ReID, summarized as follows: We proposed a Two-Stream Model which can solve the illumination adaptive person ReID problem. It can separate ReID features from lighting features to enhance ReID performance. We …
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Deep Learning Methods for Muscle Analysis From Magnetic Resonance Images
… annotations. Segmentation frameworks utilising deep learning offer a data-driven approach to automate the annotation process efficiently and accurately. However, their performance is typically dependent on the nature, distribution and size of the training data. Four studies are presented in this …
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Deep learning methods for clinical trial design, execution, and analysis
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Fast head profile estimation using curvature, derivatives and deep learning methods
… this thesis focuses on the investigation of methods to estimate head profile and posture efficiently and accurately, and results in the development and evaluation of datasets, features and deep learning models that can achieve this. Accordingly, this thesis initially investigated properties …
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Analyzing voltage sag direction using protective relays and deep-learning methods
… location, and power quality monitoring. Machine learning techniques are increasingly applied in power systems protection to enhance fault detection and classification accuracy and speed. ML algorithms can be used to analyze real-time data from sensors and other devices to detect and classify …
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Reliable image reconstruction techniques : enforcing measurement consistency in deep learning methods
… of the gravitational field. Traditional methods to solve LIPs typically involve optimization algorithms, which have been rigorously studied for decades. However, modern deep learning (DL) networks have now almost completely taken over, producing results which are far superior to previous …
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Comparing Phylogenetic and Deep Learning Methods to Predict Seed Dispersal Mode
… Dispersal modes include biotic and abiotic methods, and vary depending on traits such as seed shape, size, and color. However, globally, data on seed dispersal modes of plant species is limited, hindering our understanding of the importance of wild animals in increasing tree cover and their …
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Deep learning methods for the design and understanding of solid materials
… also poses a challenge for conventional machine learning approaches based on structure features. In this thesis, I develop a class of deep learning methods that solve various types of learning problems for solid materials, and demonstrate its application to both accelerate material design and …
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Deep Learning Methods for Predicting Fluid Forces in Dense Particle Suspensions
… on the dynamics of the system, this study trains deep learning models using micro-scale PRS data to predict drag forces on ellipsoidal particle suspensions to be applied to meso-scale and macro-scale models. Two different deep learning methodologies are employed, multi-layer perceptrons (MLP) and …
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Deep learning methods applied to anomaly detection in vehicle manufacturing and operations
… vehicles is safety. With the adoption of deep learning (DL) methods, DL-based defect detection and fault detection technology has evolved into a powerful tool with increased accuracy and autonomy compared with traditional detection technology. This thesis presents novel deep learning …
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