Zobrazeno 1 - 10
of 2 502
pro vyhledávání: '"graph learning"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Imbalanced datasets, where the minority class is underrepresented, pose significant challenges for node classification in graph learning. Traditional methods often address this issue through synthetic oversampling techniques for the minority
Externí odkaz:
https://doaj.org/article/a071c718fd6f4795ad1944e32400a4ab
Publikováno v:
Frontiers of Urban and Rural Planning, Vol 2, Iss 1, Pp 1-13 (2024)
Abstract With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more
Externí odkaz:
https://doaj.org/article/b5984607aecb4a09a4029fab80cba0c1
Autor:
Ruifan Wu, Zhipei Chen, Jiali Yu, Peng Lai, Xuanyi Chen, Anjia Han, Meng Xu, Zhaona Fan, Bin Cheng, Ying Jiang, Juan Xia
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-14 (2024)
Abstract Background Sjögren’s Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus eryt
Externí odkaz:
https://doaj.org/article/7e8e244688a449df80904d67597b7822
Publikováno v:
Journal of Theoretical and Applied Electronic Commerce Research, Vol 19, Iss 3, Pp 1756-1775 (2024)
In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing
Externí odkaz:
https://doaj.org/article/1c64116c8b554cca84a5b60b0b34457f
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 7, Pp 102129- (2024)
Multi-view graph clustering has garnered tremendous interest for its capability to effectively segregate data by harnessing information from multiple graphs representing distinct views. Despite the advances, conventional methods commonly construct si
Externí odkaz:
https://doaj.org/article/1cab78e3e1994f8dbc73f856eddc6135
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 2, Pp 1087-1113 (2024)
Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerabi
Externí odkaz:
https://doaj.org/article/a4c0ef40dd2444b38a33872befd0e601
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only pre
Externí odkaz:
https://doaj.org/article/f90c78138b4e4ccb83187efd81455c80
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-19 (2024)
Abstract This paper introduces a novel model for spectral clustering to solve the problem of poor connectivity among points within the same cluster as this can negatively impact the performance of spectral clustering. The proposed method leverages bo
Externí odkaz:
https://doaj.org/article/5227fdcb9fc641baadd993ece2000512
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2024, Iss 1, Pp 1-20 (2024)
Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, th
Externí odkaz:
https://doaj.org/article/dff0491741fc42d5a001c4f810b7511c
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-21 (2024)
Abstract Background High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug–target affinity (DTA) is a crucial stage in this process, p
Externí odkaz:
https://doaj.org/article/b15a5b0dabd34574b47aa5d41660c8b0