graph neural networks

DjangoDefects: A Multi-Granularity SZZ-Labeled Defect Dataset for Python

DjangoDefects is an inter-release defect prediction benchmark for Python, built from the full version history of the Django web framework. It covers 24 consecutive releases (1.0 to 5.2, September 2008 to April 2025). For every release it provides defect labels at four granularities, all derived from a single SZZ run:-...

Heterophily Limits Spatial Graph Encoding for PM2.5 Forecasting in IoT Smart Cities

Graph neural networks are widely assumed to improve air-quality forecasting as the spatial heterogeneity of Internet of Things monitoring networks grows.We test this on a reproducible four-network benchmark under identical preprocessing, comparing a temporal-only Transformer against three graph architectures, and we in...

ADMP-GCN: Interpretable Adaptive Message Passing Graph Learning for Patient-specific EEG Seizure Prediction with Human-in-the-Loop Validation

Automated seizure prediction based on multichannel Electroencephalography (EEG) using Artificial Intelligence (AI) is a challenging task that mandates accurate and interpretable results for improved clinical decisionmaking.However, brain connectivity modelling on the evolving patient-specific and seizure-dynamic EEG si...

Morphology-aware Tomato Leaf Disease Classification Using Differentiable Voronoi Graph Tokenization Framework

Tomato plants are valuable agricultural crops that are susceptible to various diseases that require timely diagnosis to enhance crop quality and production rates.However, existing models use rigid patching, which causes structural fragmentation and discards important structural information.Furthermore, existing models...

An adaptive congestion-aware graph neural routing framework with hybrid microcoded event-driven control for network-on-chip systems

Network-on-Chip (NoC) architectures have become a fundamental communication backbone for many-core processors, artificial intelligence accelerators, edge-computing platforms, and high-performance embedded systems. However, achieving efficient packet routing in large-scale NoCs remains challenging due to dynamic traffic...

Autonomous AI for Electronic Design Automation (AI-EDA): Reinforcement Learning Floorplanning, Graph Neural Routing, and Sub-2nm PPA Optimization

Modern Very Large Scale Integration (VLSI) semiconductor design is confronting unprecedented computational complexity as leading-edge manufacturing descends into sub-2nm Gate-All-Around (GAA) nanosheet nodes and 3D heterogeneous chiplet packaging. System-on-Chip (SoC) architectures integrating over 100 billion transist...

Performance Analysis of Ensemble and Hybrid Deep Learning Models in Predicting Agricultural Chemical Residues and Crop Outcome

The contemporary agricultural system has substantially transformed its productivity through extensive deployment of chemical pesticides, herbicides, and fertilizers. However, these practices introduce significant environmental sustainability concerns and human health risks. This paper conducts a comprehensive performan...

STOCHASTIC TRAJECTORY PROCESSES AND GRAPH NEURAL NETWORK LEARNING FOR ADAPTIVE COVERAGE IN UAV NETWORKS

This dissertation develops a framework for UAV trajectory design that bridges theoretical coverage guarantees and practical implementation constraints. The research introduces stochastic trajectory processes as a unifying paradigm, departing from deterministic optimization approaches that require precise user locations...

Graph neural network–based data assimilation for spatial reconstruction under sparse observations

SRTRL: Shared Radar-Communication Temporal Representation Learning for Beam Prediction in Vehicular and UAV mmWave Networks

Proactive beam management is essential for high-mobility millimeter-wave communication systems, where transmission latency may cause beams selected from current observations to become mismatched with the optimal beams at future transmission times. This paper investigates radar-only causal multi-horizon proactive beam p...

Global optimization workflow with compact graph neural networks for comprehensive transition-state mapping

Self-rewiring Multimodal Graph Neural Networks for Robust Autism Classification in Child Brain Imaging Operations