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 9 of 9 for “"Multimodal Data Fusion"”.
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Advances in Hierarchical Probabilistic Multimodal Data Fusion
Multimodal data fusion is the process of integrating disparate data sources into a shared representation suitable for complex reasoning. As a result, one can make more precise inferences about the underlying phenomenon than is possible with each data source used in isolation. In the thesis we adopt …
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Multimodal Data Fusion for Estimating Electricity Access and Demand
… machine learning systems for probabilistic data fusion to the problem of forecasting annual electricity demand at the countrylevel for all African countries. We provide a novel set of probabilistic forecasts for the continent while addressing missing data issues and employing a rigorous …
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Copula-based Multimodal Data Fusion for Inference with Dependent Observations
<p>Fusing heterogeneous data from multiple modalities for inference problems has been an attractive and important topic in recent years. There are several challenges in multi-modal fusion, such as data heterogeneity and data correlation. In this dissertation, we investigate inference problems with …
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Multimodal Data Fusion for Deep Learning Applications in Intracoronary Image Segmentation
… towards the construction of a multi-anatomical, multimodal segmentation and co-registration platform for intracoronary images. Although manual annotation and co-registration of intracoronary images from different modalities remain the gold standard today for facilitating the use of intravascular …
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Action Labeling in Images and Video
… Using Privileged Information" framework, multimodal data fusion, and knowledge distillation to improve deep learning models' performance. These methods are assessed for the problems of: (i) recognizing carrying actions in "visible spectrum" and "near-infrared" images, as well as (ii) …
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Evaluating Neuroimaging Modalities in the A/T/N Framework: Single and Combined FDG-PET and T1-Weighted MRI for Alzheimer’s Diagnosis
… (MRI) in diagnosing AD, where the integration of multimodal models is becoming a trend. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in …
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A Novel Approach to Indoor Environment Assessment: Artificial Intelligence of Things (AIoT) Framework for Improving Occupant Comfort and Health in Educational Facilities
… framework that bridges gaps by uti- lizing multimodal data fusion and deep learning-based prediction and classification models. These models are developed to utilize real-time multidimensional IEQ data, non-intrusive occupant feedback (MFCC features from audio recordings, video/thermal …
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USAGE OF VERY HIGH-RESOLUTION OPTICAL RGB SATELLITE IMAGERY IN GEO-INFORMATION EXTRACTION FOR FINE-SCALE MAP-MAKING
… how satellite imagery can be used as the main data for geo-information extraction, and focus on providing solutions and a new dataset to alleviate the data scarcity issues. Then, we investigate the possibility of using satellite imagery as a geo-referenced data source to extract the location …
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PATIENT SIMILARITY NETWORKS-BASED METHODS FOR MULTIMODAL DATA INTEGRATION AND CLINICAL OUTCOME PREDICTION
… relies on the ability to collect comprehensive data from each patient, covering various aspects of their disease. This includes gathering information at different levels to form a complete picture of the pathology, incorporating genomic, environmental, and lifestyle factors. Recent technological …