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425 results on '"Chen, Shangbin"'

14. Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks.

Author

Krivonosov, Mikhail, Nazarenko, Tatiana, Ushakov, Vadim, Vlasenko, Daniil, Zakharov, Denis, Chen, Shangbin, Blyus, Oleg, and Zaikin, Alexey

Subjects
GRAPH neural networks, MACHINE learning, DEEP learning, ARTIFICIAL intelligence, REPRESENTATIONS of graphs
Abstract

This paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good for addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional machine learning and Artificial Intelligence methods often struggle to find global optima due to the "curse of dimensionality". To apply Geometric Deep Learning we propose a synolitic or ensemble graph representation of the data, a universal method that transforms any multidimensional dataset into a network, utilising only class labels from training data. The paper demonstrates the effectiveness of this approach through two classification tasks: synthetic and fMRI data from cognitive tasks. Convolutional Graph Neural Network architecture is then applied, and the results are compared with established machine learning algorithms. The findings highlight the robustness and interpretability of SGNNs in solving complex, high-dimensional classification problems. [ABSTRACT FROM AUTHOR]

Published
2025
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35. Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers.

Author

Li, Maofei, Jiang, Zhihai, Liu, Shucai, Chen, Shangbin, and Tong, Xuerui

Subjects
MINE water, ELECTRIC transients, ELECTROMOTIVE force, ELECTROMAGNETIC fields, AQUIFERS
Abstract

Coal-bearing strata belong to sedimentary strata, and there are multiple aquifers. The accurate detection of deep aquifers is helpful to the safe mining of the working face. In order to provide guidance for the interpretation of the surface-to-underground transient electromagnetic method (SUTEM) that can be used to detect deep aquifers, we used theoretical analysis and numerical simulation methods in this study. Taking uniform half-spaces, single aquifers, and double aquifers as examples, we systematically studied the data characteristics and degree of influence of SUTEM under the influence of shallow aquifers. The results indicate the following: Under the influence of the primary field distribution, the x or y component of the induced electromotive force received by the underground receiving point has a positive and negative inflection point, which increases the difficulty of data interpretation, and the z component is easier to use for data interpretation. The influence of the aquifer on the early data of the underground receiving point is much greater than that of the ground receiving point, and the late influence is closer to the ground receiving point. The change in resistivity of the shallow aquifer has the greatest influence on the ability of each measuring point to detect the data of the deep aquifer; this influence is followed by change in thickness, and change in depth has the least influence on the detection capability of each measuring point. [ABSTRACT FROM AUTHOR]

Published
2024
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