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 23 for “"deep learning method"”.
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Emotion recognition in video using deep learning method with subtract pre-processing
… is to distinguished human expression using a deep learning method. This paper present a new preprocessing method to extract the features of human expression from videos, and then uses deep learning methods to analysis the human emotions. A facial expression is usually regarded as a fixed …
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Multiscale forward and inverse problems with the DGFD method and the deep learning method
… large-scale field test and inversion. A deep learning based full wave inversion method has also been developed to reconstruct the underground anomaly.</p><p>Since the gas and oil industry has very high demands for the forward modeling speed when doing inversion, the inversion model is …
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Advances in Probabilistic Deep Learning and Their Applications
Deep learning and probabilistic modeling are two machine learning paradigms with complementary benefits. Probabilistic deep learning aims to unify the two, with the potential to offer compelling theoretical properties and practical functional benefits across a variety of problems. This thesis …
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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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An Energy-Efficient Spiking CNN Implementation for Cross-Patient Epileptic Seizure Detection
… as inputs. Our convolutional neural network as a deep learning method learns a general spatially irreducible representation of a seizure to improves sensitivity, specificity, and accuracy results comparable to the state-of-the-art results. In this work, in order to avoid the inherent high …
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Modeling Molecular Structures with Intrinsic Diffusion Models
… docking. In both tasks, we construct the first deep learning method to outperform traditional computational approaches achieving an unprecedented level of accuracy for scalable programs.
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Optimization and Supervised Machine Learning Methods for Inverse Design of Cellular Mechanical Metamaterials
… an experience-free and systematic design methodology for microstructures of CMMs using an advanced stochastic searching algorithm called micro-genetic algorithm (μGA). Locally, this algorithm minimizes the computational expense of the genetic algorithm (GA) with a small population size and …
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Uncertainty and Generality of Transfer Learning Models in Predicting Signaling History
… to Infer Signaling state), a semi-supervised deep learning method that fits conditional variational autoencoders (CVAE) to single-cell RNA sequencing (scRNA-seq) data. IRIS is able to annotate cellular signaling states of individual cells using only their gene expression. Currently, IRIS has …
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Deep learning methods to study structurally heterogeneous macromolecules in vitro and in situ
… implementation, and application of tomoDRGN, a deep learning method developed to resolve structurally heterogeneous macromolecules in situ. TomoDRGN builds on the well characterized cryoDRGN method, which facilitates analysis of heterogeneous structures by cryo-EM, to cryo-ET, where I show it …
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TACTILE AND MULTISPECTRAL BIMODAL IMAGING FOR BREAST CANCER RISK ASSESSMENT
… care physicians. This work aims to develop a method to characterize breast tumors and tissue using non-invasive imaging modalities. The proposed bimodal imaging system has tactile and multispectral imaging capabilities. Tactile imaging modality characterizes tumors by esti-mating their depth, …
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Document Layout Analysis and Recognition Systems
… document. Specifically, we proposed (1) a method that converts an OCR document into a semi structured document using text attributes such as font size, font height, and boldface (in Chapter 2), (2) an image-based machine learning method that extracts Table of Contents (TOC) to provide an …
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Exploration and Comparison of Image-Based Techniques for Strawberry Detection
… the traditional computer vision technique and deep learning method.</p> <p>When strawberries are in different growth stages, there are considerable differences in their color. Therefore, various color spaces are first studied in this work, and the most effective color components are used in …
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Computational Methods to Design Broad-Spectrum Medical Countermeasures Against Antigenically Diverse Pathogens
… approaches, such as graph-based and deep learning methods, can be used to design broad-spectrum vaccines and antibodies that are effective against a wide range of pathogen variants. I focus on two complementary projects: 1) designing single-antigen vaccines to induce broad-spectrum …
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STATISTICAL METHODS FOR ANALYSIS OF HIGH-DIMENSIONAL NEUROIMAGING DATA
… within each modality can be analyzed by existing methods; however, additional information is present in the relationship between these modalities, which we call intermodal coupling (IMCo). We develop PCA-based intermodal coupling (pIMCo), a method which summarizes voxel-wise covariance structures …
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Recognizing Brain Regions in 2D Images from Brain Tissue
… of other imaging modalities. Various tools and deep learning models have been developed to automatically identify different anatomical structures in 3D MRI volumes. However, the only method that exists to segment the anatomical structures in 2D brain slices, whether they be 2D slices extracted …
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Learning Seismic Waves for Imaging the Earth
… and monitoring, and shows applications of deep learning in solving challenges in seismic imaging with either active or passive seismic data. For active data, we develop deep-learning methods to extrapolate missing low-frequency waves from band-limited seismograms. Low-frequency waves are …
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Gene Regulatory Networks at Single-Cell Resolution: an approach to exploring the impact of genomic regulation on cellular heterogeneity
… point of view, and I build a computational method to demonstrate the plausibility of inferring one gene regulatory network (GRN) for each single cell, also evaluations are conducted from multiple perspectives. In Chapter 2, I investigate data-fitting models in existing computational methods …
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Melanoma Detection Using Image Processing and Computer Vision Algorithms
… process. Increasing innovation in non-invasive methods can be of significant help in the early detection of malignant melanoma, thus minimising the need for biopsies. The initial step is to analyse and develop efficient algorithms for melanoma detection. This thesis is mainly focused on two main …
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Applied AI for QoE-Aware Video Service and Network Management
… problem and propose efficient metaheuristic methods: Boosted-GA and Boosted-PSO, for the GPU-based server selection in CG. The proposed methods simultaneously consider service providers’ profits and players’ experiences by maximizing GPU utilization. The second management system investigates …
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Machine learning techniques for sensor-based household activity recognition and forecasting
… carried out by the inhabitants. The Machine Learning field has seen significant advancements in the development of new techniques, especially regarding deep learning algorithms. Such techniques can be successfully applied to household activity signal data to benefit the user in several …
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