graph neural networks

The Drift Neural Network: A Neuro-Symbolic Cognitive Architecture for Autonomous Systems

The dominant approach to machine intelligence scales one architecture, the autoregressive transformer. A transformer is a fixed matrix of learned weights that produces output by statistical continuation. Four of its limits are structural, and adding parameters does not remove them: it has no internal test for truth, it...

Hierarchical graph networks for breast cancer subtype classification

A hierarchical Graph Neural Network (GNN) framework for ROI-level breast cancer subtype classification that represents nuclei and tissue regions as linked graph structures is presented and shows that sequential hierarchical fusion is the most effective configuration in this setting.

A dynamic graph neural network classification method incorporating machine vision features

A dynamic graph neural network classification method integrating machine vision mapping and spatiotemporal evolution that effectively improves the generalization accuracy and anti-interference capability of heterogeneous network entity classification models.

The Drift Neural Network: A Neuro-Symbolic Cognitive Architecture for Autonomous Systems

The dominant approach to machine intelligence scales one architecture, the autoregressive transformer. A transformer is a fixed matrix of learned weights that produces output by statistical continuation. Four of its limits are structural, and adding parameters does not remove them: it has no internal test for truth, it...

Supporting Data and Code for RART: A Resource-Aware Recursive Transformer for Stochastic Resource-Constrained Project Scheduling

This record provides supporting experimental data, source code, selected trained model weights, and reproducibility documentation for the manuscript “RART: A Resource-Aware Recursive Transformer for Stochastic Resource-Constrained Project Scheduling” by Cem Savas Aydin. The study evaluates RART on stochastic resource-c...

Supporting Data and Code for RART: A Resource-Aware Recursive Transformer for Stochastic Resource-Constrained Project Scheduling

This record provides supporting experimental data, source code, selected trained model weights, and reproducibility documentation for the manuscript “RART: A Resource-Aware Recursive Transformer for Stochastic Resource-Constrained Project Scheduling” by Cem Savas Aydin. The study evaluates RART on stochastic resource-c...

Data and models for leakage-controlled PFAS bioactivity prediction with chemical-space-aware routing

Code, decontaminated data, and model weights for the manuscript "Chemical-space-aware routing enhances PFAS toxicity prediction: large-scale pretraining, conditional fine-tuning, and leakage control". The archive contains the complete pipeline for training and evaluating multi-task graph neural networks (Chemprop D-MPN...

SR-CGCNN: Shared recurrent convolution in crystal graph neural networks for materials property prediction

Crystal graph neural networks predict materials properties by propagating information through local atomic environments. In conventional crystal graph convolutional neural networks (CGCNNs), this propagation depth is increased by stacking independently parameterized convolutional layers. This coupling between message-p...

From Bellman to Real-Time: Extensions to Complex Weather Regimes, Physics-Informed Optimization, and Full-Scale Validation

In our earlier methodology paper, we introduced a hierarchical framework combining graph compression, Diffusion Convolutional Recurrent Neural Networks (DCRNNs), and Multi-Agent Reinforcement Learning (MARL) to approximate Bellman’s optimality principle for real-time energy system control, validated using Palm Springs,...

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

PathSepsisNet: a pathway-aware temporal graph attention network integrating inflammatory crosstalk dynamics for early prediction of multi-organ dysfunction in sepsis