Results 141 to 150 of about 24,295 (253)

A Small-Sample Graph Neural Network Approach for Predicting Sortie Mission Reliability of Shipborne Vehicle Layouts

Journal of Marine Science and Engineering
Conventional methods for calculating sortie mission reliability of shipborne vehicle layouts suffer from excessive computational overhead, long runtimes, and large labeled data requirements.
Han Shi, Nengjian Wang, Qinhui Liu
doaj   +1 more source

A Synergistic Strategy for Data‐Constrained Deep Learning in Materials Science

Materials Genome Engineering Advances, EarlyView.
This work develops a three‐stage machine learning framework for materials property prediction, integrating data preparation, graph‐based model training, and final property inference. By synergistically integrating attention pooling, multi‐task learning, auxiliary tasks, and classification‐corrected regression, this hybrid framework provide a ...
Chun Ting Shao, Yi Chen, Shan Man Song, Jian Xu, Peipei Yang, Qing Bo Yan, Gang Su   +6 more
wiley   +1 more source

A composite‐loss graph neural network for the multivariate post‐processing of ensemble weather forecasts

Quarterly Journal of the Royal Meteorological Society, EarlyView.
The dual graph neural network (dualGNN), trained with a composite loss combining the energy score (ES) and variogram score (VS), consistently outperformed models optimized solely for ES or the continuous ranked probability score in the multivariate setting, as well as empirical copula approaches.
Mária Lakatos
wiley   +1 more source

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

Journal of Big Data
Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and ...
Bharti Khemani, Shruti Patil, Ketan Kotecha, Sudeep Tanwar   +3 more
doaj   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Path‐Based Deep Reinforcement Learning for On‐Board Routing in Satellite Constellation Networks

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT Efficient usage of available network resources is a crucial factor for broadband services in interconnected satellite constellations. To meet required quality of service standards under heavy network loads, it is essential to optimize traffic distribution among the intersatellite links. To address this challenge, we propose an adaptive traffic
Manuel M. H. Roth, Thomas Jerkovits, Anupama Hegde, Thomas Delamotte, Andreas Knopp   +4 more
wiley   +1 more source

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

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

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

A Dynamic Correlation‐Information‐Fusion‐Based Spatiotemporal Network for Traffic Flow Forecasting

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Traffic Flow Forecasting (TFF) is a foundational task in the development of Intelligent Transport Systems (ITSs). The primary challenge is to undertake a comprehensive exploration of the intrinsic dynamic spatiotemporal correlations of the road network, unveiling the long‐term evolutionary traffic trends.
Dawen Xia, Zhan Lin, Xingyan Wang, Ruixi Huang, Jinhui Hu, Yang Hu, Huaqing Li   +6 more
wiley   +1 more source