Results 71 to 80 of about 38,896 (265)

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks

MethodsX
In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being.
Bharti Khemani, Shruti Patil, Ketan Kotecha, Deepali Vora   +3 more
doaj   +1 more source

Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks

Machine Learning and Knowledge Extraction, 2020
This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption ...
Neda H. Bidoki, Alexander V. Mantzaris, Gita Sukthankar   +2 more
doaj   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

Limitless stability for Graph Convolutional Networks

CoRR, 2023
This work establishes rigorous, novel and widely applicable stability guarantees and transferability bounds for graph convolutional networks -- without reference to any underlying limit object or statistical distribution. Crucially, utilized graph-shift operators (GSOs) are not necessarily assumed to be normal, allowing for the treatment of networks on
openaire   +3 more sources

A Network Scanning Organization Discovery Method Based on Graph Convolutional Neural Network

Information
With the quick development of network technology, the number of active IoT devices is growing rapidly. Numerous network scanning organizations have emerged to scan and detect network assets around the clock.
Pengfei Xue, Luhan Dong, Chenyang Wang, Cheng Huang, Jie Wang   +4 more
doaj   +1 more source

A Deep Graph Structured Clustering Network

IEEE Access, 2020
Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature information
Xunkai Li, Youpeng Hu, Yaoqi Sun, Ji Hu, Jiyong Zhang, Meixia Qu   +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

Improved GCN Model for Inexact Graph Matching

Jisuanji kexue yu tansuo, 2020
Aiming at the problem of mining the features of topology nodes deficiently in the existing inexact graph matching, this paper proposes an improved graph convolutional network (GCN) model for inexact graph matching. Firstly, considering that the selecting
LI Changhua, CUI Liyang, LI Zhijie
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

Dual-Attention Graph Convolutional Network [PDF]

, 2020
Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity.
Xueya Zhang, Tong Zhang 0021, Wenting Zhao 0001, Zhen Cui 0001, Jian Yang 0003   +4 more
openaire   +2 more sources