Results 111 to 120 of about 22,751 (263)

Recent progress on the development of hydrogel‐based microneedles for transdermal drug delivery

VIEW, EarlyView.
This study provides a comprehensive and up‐to‐date analysis of recent progress in HMN technology, covering fundamental transdermal delivery mechanisms and design principles, biomaterials used in HMN fabrication, drug loading and release strategies, and emerging therapeutic applications.
Mohammad Muhtasim Ittisaf, Salman Farsi Taharat, Md. Sadek Miah, Hasan Tasnim Salehin, M. Azam Ali, Shoeb Ahmed   +5 more
wiley   +1 more source

GCN-TULHOR: Trajectory-User Linking Leveraging GCNs and Higher-Order Spatial Representations

CoRR
Trajectory-user linking (TUL) aims to associate anonymized trajectories with the users who generated them, which is crucial for personalized recommendations, privacy-preserving analytics, and secure location-based services. Existing methods struggle with sparse data, incomplete routes, and limited modeling of complex spatial dependencies, often relying
Khoa Tran, Pranav Gupta, Manos Papagelis
openaire   +3 more sources

Graph neural network‐based attack prediction for communication‐based train control systems

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions.
Junyi Zhao, Tao Tang, Bing Bu, Qichang Li   +3 more
wiley   +1 more source

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

Journal of Cloud Computing: Advances, Systems and Applications
In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies
Huimin Han, Harold Neira-Molina, Asad Khan, Meie Fang, Haitham A. Mahmoud, Emad Mahrous Awwad, Bilal Ahmed, Yazeed Yasin Ghadi   +7 more
doaj   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

A novel S-scheme heterojunction photocatalyst, Yb6Te5O19.2/g-C3N4: synthesis, characterization, photocatalytic activity, and mechanism

Scientific Reports
The increasing reliance on fossil fuels has led to a surge in atmospheric CO₂, intensifying global warming, whereas water contamination by organic pollutants presents another pressing global issue.
Chiing-Chang Chen, Chao-Wei Chen, Janah Shaya, Shuo-Yi Zhang, Yu-Yun Lin, Fu-Yu Liu, Jia-Hao Lin, Chung-Shin Lu   +7 more
doaj   +1 more source

Input data for GCN.

Graph neural networks (GNNs), with their ability to incorporate node features into graph learning, have achieved impressive performance in many graph analysis tasks.
Han Zhang (110653), Qingju Jiao (13272231), Jingwen Wu (80821), Nan Wang (21935), Guoying Liu (28996), Yongge Liu (3798460)   +5 more
core   +1 more source

Co-GCN for Multi-View Semi-Supervised Learning

, 2020
In many real-world applications, the data have several disjoint sets of features and each set is called as a view. Researchers have developed many multi-view learning methods in the past decade.
Wang, Wei, Li, Wen-Tao, Li, Shu
core   +1 more source

Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph contrastive learning (GCL) relies on acquiring high‐quality positive and negative samples to learn the structural semantics of the input graph. Previous approaches typically sampled negative samples from the same training batch or an irrelevant external graph.
Haoran Yang, Hongxu Chen, Xiangyu Zhao, Sixiao Zhang, Xiangguo Sun, Qian Li, Hongzhi Yin, Guandong Xu   +7 more
wiley   +1 more source

α-Fe2O3/g-C3N4 nanocomposite with type II heterojunction for methylene blue photodegradation

Chemical Physics Impact
Recently, the widespread use of industrial dyes, such as methylene blue, is raising concerns about organic pollutants. These compounds can persist in the environment, contaminating water sources and harming aquatic ecosystems.
Soufiane Hmamouchi, Ahmed El Yacoubi, Mounir El Hezzat, Brahim Sallek, Brahim Chafik El Idrissi   +4 more
doaj   +1 more source