Results 131 to 140 of about 26,406 (246)

Location‐Assisted Graph‐Based User Scheduling in Multi‐User MIMO LEO NTN Systems

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper addresses user clustering and scheduling for multi‐user MIMO low Earth orbit nonterrestrial network systems in full frequency reuse. Since the number of on‐ground user terminals is usually much higher than the number of on‐board LEO satellite antennas, user scheduling becomes a fundamental task.
Bilal Ahmad, Daniel Gaetano Riviello, Bruno De Filippo, Alessandro Guidotti, Alessandro Vanelli‐Coralli   +4 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

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang, Jian Yang, Bob Zhang, Yang Tang, Wenli Du, Bihan Wen   +5 more
wiley   +1 more source

On Shape Optimization with Large Magnetic Fields in Two Dimensions. [PDF]

J Geom Anal
Lotoreichik V, Morin L.
europepmc   +1 more source

Boosted unsupervised feature selection for tumor gene expression profiles

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 more
wiley   +1 more source

Predictive modeling for physicochemical properties of β-lactam antibiotics through eigenvalue based topological indices and non linear regression techniques. [PDF]

Sci Rep
Yuvaraj A, Kalaimurugan G, Thamizhmaran R, Tajudeen MM, Contreras RC.   +4 more
europepmc   +1 more source

Bio‐Inspired Optimisation Methods Applied to Low Carbon Power and Energy Problems: A Survey

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Bio‐inspired optimisation methods have been widely applied to complex real‐world problems, particularly in low‐carbon power and energy systems, where optimisation tasks often involve high‐dimensional, constrained and mixed‐integer characteristics.
Tianyu Hu, Shihao Zhao, Yuanjun Guo, Linxin Zhang, Zhile Yang   +4 more
wiley   +1 more source

Laplacian reconstructive network for guided thermal super-resolution. [PDF]

Sci Rep
Kasliwal A, Gakhar I, Kamani A, Seth P, Verma U.   +4 more
europepmc   +1 more source

Short‐Term Multi‐Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention‐GCN‐LSTM

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng   +4 more
wiley   +1 more source

Invariance Principle for Lifts of Geodesic Random Walks. [PDF]

J Theor Probab
Junné J, Redig F, Versendaal R.
europepmc   +1 more source