Results 81 to 90 of about 5,438,478 (196)

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

A Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection

Sensors, 2022
Pneumonia is one of the main causes of child mortality in the world and has been reported by the World Health Organization (WHO) to be the cause of one-third of child deaths in India.
Akram Ali Ali Guail, Gui Jinsong, Babatounde Moctard Oloulade, Raeed Al-Sabri   +3 more
doaj   +1 more source

Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis

CoRR, 2022
Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications. Recent studies in Document Layout Analysis usually rely on computer vision models to understand documents while ignoring other information, such as context information or relation of ...
Siwen Luo, Yihao Ding, Siqu Long, Josiah Poon, Soyeon Caren Han   +4 more
openaire   +3 more sources

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Spatial Multifeature and Dual-Layer Multihop Graph Convolution Networks for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral image (HSI) classification constitutes a crucial research direction within the domain of remote sensing. Convolutional neural networks (CNNs) and graph convolutional network (GCN) have exhibited outstanding classification performance in ...
Xiangyue Yu, Ning Li, Di Wu, Zheng Li, Zhenyuan Wu, Ximing Ma   +5 more
doaj   +1 more source

Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks

Journal of NeuroEngineering and Rehabilitation, 2022
Background Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson’s disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG.
Benjamin Filtjens, Pieter Ginis, Alice Nieuwboer, Peter Slaets, Bart Vanrumste   +4 more
doaj   +1 more source

Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation

Advanced Intelligent Discovery, EarlyView.
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei, Sadman Sadeed Omee, Rongzhi Dong, Nihang Fu, Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Meiling Xu, Ming Hu, Chris Wolverton, Jianjun Hu   +9 more
wiley   +1 more source

Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

IEEE Access, 2020
Reliable online transient stability assessment (TSA) is fundamentally required for power system operation security. Compared with time-costly classical digital simulation methods, data-driven deep learning (DL) methods provide a promising technique to ...
Jiyu Huang, Lin Guan, Yinsheng Su, Haicheng Yao, Mengxuan Guo, Zhi Zhong   +5 more
doaj   +1 more source

A survey of graph convolutional networks (GCNs) in FPGA-based accelerators

Journal of Big Data
AbstractThis survey overviews recent Graph Convolutional Networks (GCN) advancements, highlighting their growing significance across various tasks and applications. It underscores the need for efficient hardware architectures to support the widespread adoption and development of GCNs, particularly focusing on platforms like FPGAs known for their ...
Procaccini, Marco, Sahebi, Amin, Giorgi, Roberto   +2 more
openaire   +4 more sources

Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation

Advanced Intelligent Systems, EarlyView.
Robot arms lifting a large object face a trade‐off: centralized controllers explode in parameters, while decentralized ones cannot coordinate. A GNN resolves this—each arm runs its own network but acts on the full team state, achieving centralized‐level coordination with decentralized execution. Trained across team sizes, a single policy scales to four‐
Tong Chen, Bo Fu, Dawn Tilbury, Kira Barton   +3 more
wiley   +1 more source