Search Results - "graph neural network"

Accurate drug-drug interaction (DDI) prediction is critical for ensuring patient safety and guiding clinical decision-making. Existing methods often rely on single-view molecular representations, limiting their ability to capture the complex structural and spatial properties of drugs. In this study,...

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Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural ne...

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Phishing emails remain a critical cybersecurity challenge, demanding detection frameworks that capture both textual semantics and structural relationships in email data. This study introduces PhishingGNN, a hybrid model that integrates DistilBERT for context-aware text analysis with Graph Attention...

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This paper proposes a kind of recommendation algorithm for agricultural commodities with fusion representation, in response to the issue of unexpected results on agricultural product e-commerce platforms due to the strong seasonality and regionality of products, as well as the variable user behavior...

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Automatic recognition systems (ARS) have been proposed in scientific and technological research for the care and preservation of endangered species; these systems, consisting of Internet of Things (IoT) devices and object-recognition techniques with artificial intelligence (AI), have emerged as prop...

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The flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often struggle with scalability and efficiency when solv...

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Remaining useful life (RUL) prediction of cutting tools plays an important role in modern manufacturing because it provides the criterion used in decisions to replace worn cutting tools just in time so that machining deficiency and unnecessary costs will be restrained. However, the performance of ex...

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Existing traffic flow modeling approaches typically rely on real-time observations, such as road sensors or GPS trajectories, which constrain their research scope and application scenarios. This study proposes a novel cross-spatiotemporal graph-based network method to rapidly reconstruct traffic flo...

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<b>Background:</b> The ability of Graph Neural Networks (GNNs) to analyse brain structural patterns in various kinds of neurodegenerative diseases, including Parkinson’s disease (PD), has drawn a lot of interest recently. One emerging technique in this field is brain age prediction, whic...

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This paper proposes the application of explanation methods to enhance the interpretability of graph neural network (GNN) models in fault location for power grids. GNN models have exhibited remarkable precision in utilizing phasor data from various locations around the grid and integrating the system...

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Abstract In the rolling production of steel, predicting the performance of new products is challenging due to the low variety of data distributions resulting from standardized manufacturing processes and fixed product categories. This scenario poses a significant hurdle for machine learning models,...

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Traffic event detection plays an essential role in flexible decision-making for sensor-cloud system(SCS). Since a social traffic event is often described by multiple social media texts, it is significant to perform traffic event detection on streaming social media texts. However, these social media...

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The inherent complexity and heterogeneity of the Internet of Things (IoT) ecosystem present significant challenges for developing effective intrusion detection systems. While graph deep-learning-based methods have shown promise in cybersecurity applications, existing approaches primarily construct g...

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High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition. Our approach generates graph representations dynam...

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Kinase is an enzyme responsible for cell signaling and other complex processes. Mutations or changes in kinase can cause cancer and other diseases in humans, including leukemia, neuroblastomas, glioblastomas, and more. Considering these concerns, inhibiting overexpressed or dysregulated kinases thro...

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The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Explicitly, in CS-SFIM, the information bits are mapped to both spatial- and frequenc...

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Delivering realistic, real-time virtual experiences in the Metaverse demands computationally intensive rendering tasks with strict latency constraints. These challenges are further intensified by dynamic behavior and heterogeneity of network resources. While traditional machine learning (ML) methods...

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Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI...

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Predicting mortality risk in the Intensive Care Unit (ICU) using Electronic Medical Records (EMR) is crucial for identifying patients in need of immediate attention. However, the incompleteness and the variability of EMR features for each patient make mortality prediction challenging. This study pro...

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Existing intelligent exception diagnosis methods face challenges in unstable feature extraction and difficulty detecting anomalies in high-dimensional spaces. To address these limitations, we propose CGNN&#x2014;a novel anomaly detection framework integrating causal reasoning with graph neural n...

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