Dissertation/ Thesis
Dynamic graph neural network framework for real-time multi-modal data analysis and predictive modeling
| Τίτλος: | Dynamic graph neural network framework for real-time multi-modal data analysis and predictive modeling |
|---|---|
| Συγγραφείς: | Almousa, Ghadah Fahad M. |
| Συνεισφορές: | Lee, Yugyung, 1960- |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | University of Missouri: MOspace |
| Θεματικοί όροι: | Real-time data processing, Predictive analytics, Neural networks (Computer science) -- Data processing, Dissertation -- University of Missouri--Kansas City -- Computer science |
| Περιγραφή: | Title from PDF of title page, viewed February 4, 2026 ; Dissertation advisor: Yugyung Lee ; Vita ; Includes bibliographical references (pages 145-162) ; Dissertation (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2025 ; In recent years, Graph Neural Networks (GNNs) have become increasingly prominent for analyzing complex, interconnected data across fields such as transportation, social networks, and cybersecurity. Despite their advancements, many existing GNN models struggle to capture the intricate interactions among temporal, spatial, and domain-specific knowledge, particularly as these factors evolve dynamically, while also accounting for the complexities of multi-modal data in real time, with current GNN architectures often falling short in leveraging cross-modal correlations. We present a novel Dynamic Graph Neural Networks (DGNNs) Framework that integrates Partial Differential Equations (PDEs), temporal-spatial modeling, and domain-specific knowledge to address these gaps. By enabling real-time processing of multi-modal data, this framework bridges real-world dynamic systems with the evolving landscape of AI and machine learning applications. This interdisciplinary approach uniquely advances AI, machine learning, and big data analytics by harmonizing spatial-temporal dynamics, domain customization, and multi-modality integration in a cohesive framework. GNNs have become essential tools for analyzing complex, interconnected data in domains such as transportation, social networks, and cybersecurity. However, current GNN models often struggle to effectively capture the dynamic interactions of temporal, spatial, and domain-specific knowledge, especially when processing multi-modal data in real time. This dissertation presents the development of a DGNNs Framework designed to overcome these challenges, illustrated through extensive use cases. For instance, in traffic prediction, experiments using datasets such as Performance Measurement System Bay Area (PEMS-BAY), Metropolitan ... |
| Τύπος εγγράφου: | thesis |
| Περιγραφή αρχείου: | xvii, 164 pages; application/pdf |
| Γλώσσα: | English |
| Relation: | https://hdl.handle.net/10355/110328 |
| Διαθεσιμότητα: | https://hdl.handle.net/10355/110328 |
| Αριθμός Καταχώρησης: | edsbas.E02C8E25 |
| Βάση Δεδομένων: | BASE |
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