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 55 for “"multimodal data"”.
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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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Learning without Expert Labels for Multimodal Data
… due to the availability of large-scale labeled datasets, obtaining labeled datasets at the required granularity is challenging in many real-world applications, especially in scientific domains, due to the costly and labor-intensive nature of generating annotations. Hence, there is a need to …
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Multimodal Data Space for Cooperative Intelligent Transport Systems
… and universal way to store and exchange data in such a traffic system. Furthermore, multimodal scenarios where different types of vehicles (e.g., cars and Unmanned Aerial System) interact with each other, are increasingly emerging. To overcome these challenges, this thesis presents a set …
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Multimodal data analysis applied to a medical setting
… have traditionally been studied using genetic data, or images alone. To understand the biology of such diseases, joint analysis of multiple data modalities could provide interesting insights. We propose the use of canonical correlation analysis (CCA) as a preliminary discovery tool for …
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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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Learning Semantic Information from Multimodal Data using Deep Neural Networks
… has been digitized to form an immense database distributed across the Internet. This can also be referred to as Big data, a collection of data that is vast in volume and still growing with time. Nowadays, we can say that Big data is everywhere. We might not even realize how much it …
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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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Tools and Methods to Analyze Multimodal Data in Collaborative Design Ideation
… make sense of unstructured verbal and sketch data generated during collaborative design, with a view to better understand these collaborative and cognitive processes. This thesis has three main contributions.
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Multimodal Data Integration for Real-Time Indoor Navigation Using a Smartphone
… traversal paths between nodes. A Wi-Fi/cellular-data connectivity map, a beacon signal strength map, a 3D visual model (with destinations and landmarks annotated) are collected while a modeler walks through the building, and then registered with the floorplan of the building. The …
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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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Machine learning-driven integration of multimodal data for deciphering breast cancer heterogeneity
… BC heterogeneity based on the multi-modal biodata using the existing computational data analysis techniques. The first challenge is how to effectively combine the multi-modal biodata and find comprehensive and interpretable representations from them. Another challenge is how to address the …
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Artificial Intelligence (AI)-based Semantic Communications with Multimodal Data: Framework and Implementation
… the so-called "semantics" or meaning behind the data. To date, existing works in this area either focus on multimodal approaches only and omit context-aware recovery or embed it in cross-modal settings, such as audio-to-video, rather than providing a unified, modality-agnostic method. These works …
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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 …
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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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Domain-specific adaptation of large language models and integration with knowledge graph analytics for enhanced bridge maintenance decision making
… system rehabilitation needs. Despite extensive data collection efforts by transportation agencies, existing data-driven models for bridge condition assessment and maintenance decision making remain limited. Most models rely primarily on abstract data from single sources, such as the National …
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ADAPTIVE FRAMEWORKS FOR KNOWLEDGE EXTRACTION IN HETEROGENEOUS DATA ENVIRONMENTS
The proliferation of unstructured, multimodal data presents a significant challenge for effective knowledge extraction, due to the heterogeneous nature and the complexity of extracting meaningful patterns in environments presenting diverse data types. This thesis proposes SHIFT, the first …
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Analyzing Multimodal Interactions through Improved Partial Information Decomposition Estimation
Multimodal AI aims to build comprehensive models by integrating information from diverse sensory inputs such as text, audio, and vision. However, significant challenges remain in understanding how these different modalities interact and contribute to downstream tasks. In particular, we seek to …
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Modelling prognostic trajectories in Alzheimer’s disease
… to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in …
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Artificial Intelligence for System Medicine: Methods and Applications
… availability of large-scale electronic health data and artificial intelligence-based technologies. In particular, integration across different patient characteristics to optimize, learn, and plan simultaneously across multiple medical tasks of interest, or what we call system medicine, provides …
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Learning to Manipulate Novel Objects for Assistive Robots
… algorithms that learn shared representations of multimodal data and model full sequences of complex motions. We demonstrate our approach on several different applications: understanding human activities in unstructured environment, synthesizing manipulation sequences for under-specified tasks, …
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