Results 131 to 140 of about 24,295 (253)

Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China

Scientific Reports
Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network
Yannan Zha, Yao Yang
doaj   +1 more source

Framework GNN-AID: Graph Neural Network Analysis, Interpretation and Defense

Proceedings of the AAAI Conference on Artificial Intelligence
The rising demand for Trusted AI (TAI) underscores the need for interpretable and robust models, yet existing tools rarely support graph-structured data or integrate interpretability with security. At the same time, Graph Neural Networks (GNNs) deliver state-of-the-art performance on numerous graph tasks.
Kirill Lukyanov, Mikhail Drobyshevskiy, Georgii Sazonov, Mikhail Soloviov, Ilya Makarov   +4 more
openaire   +2 more sources

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model

Energy Science &Engineering, EarlyView.
ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes
Iman Baghaei, Mojtaba Mirhosseini, Alireza Zahedi   +2 more
wiley   +1 more source

GNN-IDS: Graph Neural Network based Intrusion Detection System

Proceedings of the 19th International Conference on Availability, Reliability and Security
eSSENCE - An eScience ...
Zhenlu Sun, André M. H. Teixeira, Salman Toor   +2 more
openaire   +2 more sources

Molecular theranostics: principles, challenges and controversies

Journal of Medical Radiation Sciences, Volume 72, Issue 1, Page 156-164, March 2025.
Molecular theranostics offers a powerful tool to drive precision medicine in nuclear oncology. While theranostics is not a new principle in nuclear medicine, recent advances in instrumentation and radiopharmacy have driven a reinvigoration and a broader suite of applications.
Geoffrey Currie
wiley   +1 more source

Hybrid GNN-Based Link Prediction Model for Identifying Drug-Related Organized Crime Groups on Twitter

IEEE Access
In this paper, we propose a Graph Neural Network (GNN)-based link prediction model to analyze the interconnections of Drug-related Organized Crime Groups (DOCG) on X (formerly Twitter) and to identify their potential associations.
Eun-Young Park, Hyeon-Woo Lee, Jiyeon Kim   +2 more
doaj   +1 more source

Benchmarking Large Language Models for Polymer Property Predictions

Macromolecular Rapid Communications, EarlyView.
Large language models (LLMs) are fine‐tuned on polymer thermal property datasets to directly predict glass transition, melting, and decomposition temperatures from SMILES inputs. Compared to state‐of‐the‐art models such as Polymer Genome, polyGNN, and polyBERT, LLMs achieve competitive yet lower accuracy.
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad   +3 more
wiley   +1 more source

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect

Scientific Reports
In this study, we investigate appropriate machine learning methods for predicting agricultural commodity prices. Since environmental factors including weather affect price fluctuations of agricultural commodities, we constructed a multivariate time ...
Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee   +6 more
doaj   +1 more source