Results 71 to 80 of about 37,604 (258)

A review on the applications of graph neural networks in materials science at the atomic scale

Materials Genome Engineering Advances
In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong   +4 more
doaj   +1 more source

Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network

Alexandria Engineering Journal
The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital ...
Yuxin Fan, Tingting Fu, Nikolai Izmailovich Listopad, Peng Liu, Sahil Garg, Mohammad Mehedi Hassan   +5 more
doaj   +1 more source

Functional Mapping of Neurodevelopmental Disease Pathways to Key Neurodevelopmental Processes Represented in the Developmental Neurotoxicity In Vitro Testing Battery

Advanced Science, EarlyView.
Human‐relevant methods are essential for modern chemical safety assessment. This study helps define the capabilities and boundaries of an in vitro testing battery for developmental neurotoxicity by exploring its biological applicability domain. By linking neurodevelopmental disease‐related pathways to key neurodevelopmental processes, the work enhances
Eliska Kuchovska, Kristina Bartmann, Georgea Raad, Mats Schade, Luiz Ladeira, Arif Dönmez, Jördis Klose, Nicolai Görts, Denis Polozij, Lynn‐Christin Saborowski, Farina Bendt, Bernard Staumont, Liesbet Geris, Katharina Koch, Ellen Fritsche   +14 more
wiley   +1 more source

Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs

IEEE Access, 2020
Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li, Xiaowei Qin, Xiaodong Xu, Dujia Yang, Guo Wei   +4 more
doaj   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Graph Kernel Neural Networks

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.
Bicciato, Alessandro, Rodolà, Emanuele, Cosmo, Luca, Bronstein, MM, Minello, Giorgia, Bronstein, Michael, Rodolà, E, Torsello, Andrea, Rossi, Luca, Torsello, A, Minello, G, Bicciato, A, Rossi, L, Cosmo, L, Bronstein, Michael M.   +14 more
core   +1 more source

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

Advanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu, Wanpeng Zou, Yuekai Li, Jiayi Wang, Mingxuan Cai, Hongmin Cai   +5 more
wiley   +1 more source

SMS spam detection using BERT and multi-graph convolutional networks

International Journal of Intelligent Networks
The surge in smartphone usage has significantly increased Short Message Service (SMS) traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches.
Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma   +3 more
doaj   +1 more source

Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks

, 2021
Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which ...
Victor, Zavala, Reid, Van Lehn, Tianyi, Jin, Shiyi, Qin   +3 more
core   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source