graph neural networks

自适应图神经网络的结构优化

This paper presents a novel approach to self-adaptive graph neural networks, aiming to automatically optimize the network structure based on data distribution. Traditional graph neural networks often require manual design of the network architecture, limiting their adaptability. Our proposed method employs an adaptive...

Title: Adaptive Graph Neural Networks for Bayesian Inference

This paper introduces an Adaptive Graph Neural Network (AGNN) designed to enhance Bayesian inference, particularly within complex, high-dimensional datasets. Traditional graph neural networks struggle with data complexities, often requiring extensive hyperparameter optimization. We propose an innovative AGNN architectu...

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

Broadly, CIFM shows how generative artificial intelligence methods can be applied to model emergent behavior in highly interacting biological systems, yielding new approaches to fundamental understanding of tissue behavior as well as large-scale therapeutic design.

Advanced AI-Based Multi-Stage Deep Learning Architecture for Robust Image Segmentation and Comprehensive Classification of Interstitial Lung Disorders in Next-Generation Medical Diagnostics

Experimental results demonstrate better and balanced classification performance, and indicate that the proposed framework maintains balanced predictive performance across different ILD categories.

Beyond Job Titles: AI and Machine Learning Architectures Enabling Skills-Based Workforce Intelligence in Enterprise Organizations

Advances in AI and ML frameworks that can enable enterprise organizations to capitalize on skills-based workforce intelligence are reviewed and a number of ML-centric metrics to the state of the art are applied.

Neural Signatures of Conscious Experience During Sleep: A Serial Awakening Study Using High-Density EEG

Artificial Intelligence (AI) Uses in Stock Market

The use of AI in stock market forecasting has been analyzed, with special emphasis on the NEPSE, and the necessity of implementing AI in decision-making processes has been emphasized here.

Hierarchical dynamic spatio-temporal graph neural network for aerial group target intention prediction

HMR uses three-layer encoding and cross-granularity bidirectional attention for multi-level feature fusion for multi-level feature fusion; SA-DHAT ensures the consistent modeling of heterogeneous formations relying on type-specific projection and adaptive temperature scaling; IEA-TM captures multi-scale tactical dynami...

Multi-Source Heterogeneous Data Fusion in Smart Industrial Systems: Intelligent Monitoring, Predictive Quality Assessment, and Fault Diagnosis for Safer Operations

In this paper, it was concluded that MSHDF is a radical paradigm shift in operational intelligence that can be applied in manufacturing, energy, transportation, and infrastructure fields.

Adaptive Decision Intelligence through Retrieval-Augmented Multi-Agent Large Language Models

A comprehensive five-layer framework comprising foundation benchmarks, dynamic hybrid retrieval, multi-agent collaboration with weighted consensus, knowledge graph evolution through graph neural networks, and adaptive human-AI interaction is proposed, establishing a robust foundation for trustworthy, scalable, and trul...

High-dimensional dynamic coupled graph spectral physics-informed neural network for power system prediction

A novel framework termed as High-Dimensional Dynamic coupled Graph Spectral Physics-Informed Neural Network (HDGSPINN), which introduces a Spatiotemporal Graph Spectral Attention (STGSA) mechanism to capture complex spatiotemporal dependencies.

Physics-Informed Spatiotemporal Deep Learning for Cable Fault Detection and Localization in Smart Power Grids

This study proposes a physics-informed spatiotemporal deep learning framework for fault detection and localization in medium-voltage cable systems that embeds telegrapher-equation residual constraints and distributed-generation boundary consistency within a physics-informed neural network to incorporate physical knowle...