Results 141 to 150 of about 37,604 (258)

Graph Neural Network, ChebNet, Graph Convolutional Network, and Graph Autoencoder: Tutorial and Survey

This is a tutorial paper on graph neural networks including ChebNet, graph convolutional network, graph attention network, and graph autoencoder. It starts with Laplacian of graph, graph Fourier transform, and graph convolution. Then, it is explained how
Benyamin Ghojogh, Ali Ghodsi
core   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks

This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology.
Liason, Claudia, Ancelotti, Amy
core  

Attributed Graph Classification via Deep Graph Convolutional Neural Networks

, 2019
From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep ...
Suresh, Susha Pozhampallan
core  

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks. [PDF]

Front Physiol, 2021
Meister F, Passerini T, Audigier C, Lluch È, Mihalef V, Ashikaga H, Maier A, Halperin H, Mansi T.   +8 more
europepmc   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction. [PDF]

RSC Adv, 2023
Suviriyapaisal N, Wichadakul D.
europepmc   +1 more source

Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper

Advanced Intelligent Discovery, EarlyView.
This study presents an automated system integrating a capillary force gripper and machine learning‐based object detection for sorting and placing submillimeter objects. The system achieved stable and simultaneous manipulation of four object types, with an average task time of 86.0 seconds and a positioning error of 157 ± 84 µm, highlighting its ...
Satoshi Ando, Naoto Watanabe, Chihiro Sekine, Shogen Sekiguchi, Ohmi Fuchiwaki   +4 more
wiley   +1 more source

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. [PDF]

Sci Rep, 2021
Sakai M, Nagayasu K, Shibui N, Andoh C, Takayama K, Shirakawa H, Kaneko S.   +6 more
europepmc   +1 more source