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 27 for “"Graph Convolutional Network"”.
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Protein Function Prediction Using Graph Convolutional Network
… integrating protein language models (PLMs) and graph convolutional networks (GCNs), addressing the limitations of traditional methods that rely heavily on sequence similarity. The proposed model leverages diverse protein features, including sequences, protein-protein interaction (PPI) networks, …
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A Graph Convolutional Network approach for enhancing Set Covering Problem solvers
… for large instances. This study proposes a Graph Convolutional Network (GCN) to approximate optimal solutions for SCP. A bipartite graph representation of SCP is employed to predict node priority, serving as a warm start for the Gurobi solver. The GCN is trained on solutions from a classical …
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Discrete spring–mass system inversion of lung stiffness from 4DCT with adjoint regularization and graph convolutional network
… dimensional CT (4DCT) without requiring elastography labels. At its core is a discrete spring–mass system (SMS) formulation that approximates small strain linear elasticity on a tetrahedral mesh. Each respiratory phase is modeled as a quasi static equilibrium state, and an axis aligned …
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Improving Text Classification Using Graph-based Methods
… and high ambiguity that result from Arabic orthography. Thus, Arabic natural language processing is challenging. Several studies employ Long Short- Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), but Graph Convolutional Networks (GCNs) have not yet been investigated for the …
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Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors
… is the subject of this dissertation. A graph convolutional neural network for crime prediction, a multitask learning system for traffic incident prediction with spatiotemporal feature learning, social media-based transportation event detection, and a graph convolutional network-based …
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Peer evaluation with graph neural networks
… peer assessment as multi-relational weighted networks that can represent a variety of peer assessment setups, and capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph convolutional network which can learn assessment patterns …
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Hybrid architecture for human action recognition using skeleton data
… a deep learning architecture, incorporating a Graph Convolutional Network (GCN) backbone combined with a partitioning transformer, that achieves results comparable to the state-of-the-art methods in skeleton based multi-person, multiview human action recognition. By leveraging attention-based …
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CLASSIFYING TCP NETWORK TRAFFIC FLOWS VIA TRAFFIC INTERACTION GRAPHS AND MACHINE LEARNING
Detecting malicious traffic on networks is a critical problem facing the Department of Defense. In this thesis we utilize cutting edge machine learning techniques to detect malicious network traffic. We begin with two real-world datasets. First, real internet traffic collected on the NPS Enterprise …
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Leveraging Basis Alignment to create a Generalized Multi-Relational Graph Convolution Network in the Federated Setting
Knowledge graphs have seen a significant rise in popularity and usage in recent years with many real-world applications taking advantage of their ability to model interlinked data easily. In general, many institutions maintain their own knowledge graphs, however these graphs tend to suffer from …
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Visual question answering using external knowledge
… to misconceptions due to synonyms and homographs. To overcome these shortcomings, we introduce two new approaches in this work. We develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging …
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On Modeling Dependency Dynamics of Sequential Data: Methods and Applications
… via multi-context attentional recurrent neural networks, and traffic incident impact forecasting via hierarchical spatiotemporal graph neural networks. For the first of these methods, the existing transit service disruption detection methods usually suffer from two significant shortcomings: 1) …
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Human-Centric Spatio-temporal Modeling and Analysis for Multimedia Data
… data sphere, my research contributes a novel network specifically designed for 2D Unmanned Aerial Vehicle (UAV) imagery. This innovation adeptly tackles the challenges of varying weather and lighting conditions, paving the way for reliable action detection under a broad spectrum of …
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Hyperbolic graph embedding of magnetoencephalography brain networks to study brain alterations in patients with subjective cognitive decline
… classification tasks. Using a Hyperbolic Graph Convolutional Network (HGCN), we embed functional brain connectivity graphs derived from magnetoencephalography data to a Poincare disk instead of traditional Euclidean space. The Poincare disk is a negatively curved unit disk that encourages …
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Graph neural network approaches and real-time unsupervised learning for anomaly detection in vehicular networks
… or impaired, remaining leading causes. Vehicular networks, as a core component of intelligent transportation systems (ITS), enable real-time vehicle–infrastructure communication through Cooperative Awareness Messages (CAMs), offering opportunities to detect anomalies in both driving behavior and …
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Spatial-Temporal Data Modeling with Graph Neural Networks
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that …
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Data-driven depth and 3D architectural layout estimation of an interior environment from monocular panoramic input
… My first contribution consists in a deep neural network architecture for estimating a depth map from a single monocular indoor panorama, operating directly on the equirectangular projection. Leveraging the characteristics of indoor 360-degree images and recognizing the impact of gravity on indoor …
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Learning to Adapt Neural Networks Across Visual Domains
… using all the source domain data into a single network, leading to better generation of target-like source data. (iii) We address multi-target DA by learning a single classifier for all of the target domains. Our proposed framework exploits feature aggregation with a graph convolutional network …
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Inferential measurement for integrity and security of cyber-physical systems.
… to safeguard the sensors and Internet of Things network. There are two main techniques for developing an inferential measurement system: model-driven and data-driven approaches. Data-driven models generally outperform model-driven ones by leveraging data for decision-making. However, they …
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Data-Driven Routing for Autonomous Trucks: Learning from Human Behavior with Context Awareness and Privacy Protection
… develops the Multi-Task Context-Based GRU Graph Convolutional Network (MT-C2G) for predicting truck traffic under extreme weather. MT-C2G integrates Graph Convolutional Networks (GCNs) for spatial structure, Gated Recurrent Units (GRUs) for temporal dynamics, and attention mechanisms for …
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Novel Machine Learning Models Based Uncertainty Estimation and Sequential Predictions for Blockchain Networks
… address to con- duct illicit activities over the network. Consequently, this double-edged sword technology urges the necessity of analysing blockchain data to detect illicit activities. In the existing literature, visual analytics have been widely used to gain useful insights from large-scale …
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