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A Data-centric graph neural network for node classification of heterophilic networks
In the real world, numerous heterophilic networks effectively model the tendency of similar entities to repel each other and dissimilar entities to...
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Improving academic performance predictions with dual graph neural networks
Academic performance is a crucial issue in the field of Online learning analytics. While deep learning-based models have made significant progress in...
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DRGN: a dynamically reconfigurable accelerator for graph neural networks
Graph neural networks (GNNs) have achieved great success in processing non-Euclidean geometric spatial data structures. However, the irregular memory...
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Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts
The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault...
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Anime Link Prediction Using Improved Graph Convolutional Networks
Graph convolutional neural networks (GCNs) have made significant strides in recent years including social network analysis, recommendation systems,...
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Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph
As the main channel for people to obtain information and express their opinions, online media generate a huge amount of unstructured news documents...
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BKGNN-TI: A Bilinear Knowledge-Aware Graph Neural Network Fusing Text Information for Recommendation
Knowledge graph (KG)-based recommendation methods effectively alleviate the data sparsity and cold-start problems in collaborative filtering. Among...
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A graph-powered large-scale fraud detection system
Graph-powered fraud detection is a common issue in various areas, such as e-commerce, banking, insurance and social networks, where data can be...
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Efficient Hardware Acceleration of Emerging Neural Networks for Embedded Machine Learning: An Industry Perspective
As neural networks become more complex, the energy required for doing training and inference has resulted in a noticeable shift towards adopting...
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An improved model combining knowledge graph and GCN for PLM knowledge recommendation
Faced with the challenges of intelligently managing the entire product lifecycle, traditional management techniques no longer meet the complex...
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An image classification algorithm for football players’ activities using deep neural network
Football (soccer) stands as the world’s most popular sport, enthralling millions of fans globally. Within the dynamic sphere of modern football,...
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A big data and neural networks driven approach to design students management system
Designing a Student Management System based on big data and deep learning is paramount in the modern educational landscape. This innovative approach...
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Systematic Literature Review of Various Neural Network Techniques for Sea Surface Temperature Prediction Using Remote Sensing Data
The popularity of using various neural network models and deep learning-based models to predict environmental temperament is increasing due to their...
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Enhancing Time Series Analysis with GNN Graph Classification Models
The use of Graph Neural Networks (GNNs) in time series analysis is on the rise, yet the application of GNN Graph Classification in this field remains...
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Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain
As the core competitiveness of the national industry, large-scale equipment such as ships, high-speed rail and nuclear power equipment, their...
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In-memory computing: characteristics, spintronics, and neural network applications insights
In today's digital computing landscape, In-Memory Computing (IMC) has emerged as a revolutionary approach to tackling critical energy efficiency and...
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Identification of Locations in Mecca using Image Pre-Processing, Neural Networks and Deep Learning
Every year, more than two million Muslim pilgrims from all over the world visit Mecca to perform Hajj worship. The challenge of location...
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A GNN-based proactive caching strategy in NDN networks
As people spend more time watching and sharing videos online, it is critical to provide users with a satisfactory quality of experience (QoE)....
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A novel intelligent Fuzzy-AHP based evolutionary algorithm for detecting communities in complex networks
The realm of complex network analysis is witnessing a surge in research focus on community detection. Numerous algorithms have been put forth, each...
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Evaluating E-learning Engagement Through EEG Signal Analysis with Convolutional Neural Networks
Amidst the COVID-19 pandemic, online e-learning platforms have experienced a surge in popularity, serving a diverse audience across multiple...