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Titlebook: Computational Science – ICCS 2024; 24th International C Leonardo Franco,Clélia de Mulatier,Peter M. A. Slo Conference proceedings 2024 The

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书目名称Computational Science – ICCS 2024
副标题24th International C
编辑Leonardo Franco,Clélia de Mulatier,Peter M. A. Slo
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Science – ICCS 2024; 24th International C Leonardo Franco,Clélia de Mulatier,Peter M. A. Slo Conference proceedings 2024 The
描述.The 7-volume set LNCS 14832 – 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2–4, 2024...The 155 full papers and 70 short papers included in these proceedings were carefully reviewed and selected from 430 submissions...They were organized in topical sections as follows:..Part I:. ICCS 2024 Main Track Full Papers;..Part II:. ICCS 2024 Main Track Full Papers;..Part III:. ICCS 2024 Main Track Short Papers; Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks and Applications; Artificial Intelligence and High-Performance Computing for Advanced Simulations;..Part IV:. Biomedical and Bioinformatics Challenges for Computer Science; Computational Health;..Part V.: Computational Optimization, Modelling, and Simulation; Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine; Machine Learning and Data Assimilation for Dynamical Systems; Multiscale Modelling and Simulation;..Part VI:. Network Models and Analysis: From Foundations to Artificial Intelligence; Numerical Algorithms and Computer Arithmetic for Computational Scie
出版日期Conference proceedings 2024
关键词Modelling; Simulation; Big Data; Computational Science; Computational Methods; Numerical Algorithms; Compl
版次1
doihttps://doi.org/10.1007/978-3-031-63778-0
isbn_softcover978-3-031-63777-3
isbn_ebook978-3-031-63778-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

书目名称Computational Science – ICCS 2024影响因子(影响力)




书目名称Computational Science – ICCS 2024影响因子(影响力)学科排名




书目名称Computational Science – ICCS 2024网络公开度




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书目名称Computational Science – ICCS 2024被引频次




书目名称Computational Science – ICCS 2024被引频次学科排名




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书目名称Computational Science – ICCS 2024年度引用学科排名




书目名称Computational Science – ICCS 2024读者反馈




书目名称Computational Science – ICCS 2024读者反馈学科排名




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Dimitrios Kodokostas,Sofia Lambropoulouending on several factors like subtype of tumours or associated prognosis. However, the immune system’s efficiency depends on the local microenvironment and requires region-specific trials with a reduced number of samples. To minimise this drawback and improve the accuracy of patient prognosis predi
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Ioannis Diamantis,Sofia Lambropoulouas been attracting many research studies recently. Such assessment is particularly critical with the recent advances related to virtual environments and AI tools in addressing the need to expand the education and training in the medical domains. Network models and population analysis methods have be
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Stathis Antoniou,Sofia Lambropoulouat employ the metric structure in the embedding space as a proxy for the topological structure of the data. In this paper, we explore several aspects that affect the quality of a vertex embedding of graph-structured data. To this effect, we first present a family of flexible distance functions that
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Operator Theory: Advances and Applicationsthe CONN toolbox, a widely recognized tool, to extract functional networks from the fMRI data. Subsequently, these networks were aligned using MULTIMAGNA++, a global multiple alignment software, to ensure consistency across individual datasets. The aligned data was then subjected to a clustering ana
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https://doi.org/10.1007/978-3-7643-8401-2s representation has found its way into a wide range of disciplines, particularly in the fields of neuroscience and neuroimaging. Human brain modelling made possible the identification of the basis for the construction of morphological, structural and functional brain connectivity networks..In this
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https://doi.org/10.1007/978-1-4684-0296-4etric functions and rotating vectors without using complex calculations. The authors propose two modifications for improving the classical CORDIC algorithm: completing iterations with one-directional rotation of the vector at the final stages and choosing a scaling factor value that can be implement
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https://doi.org/10.1007/978-1-4684-0296-4estions about the best representations of hyperbolic spaces arise, as each representation comes with some numerical instability. This paper compares various 2D and 3D hyperbolic geometry representations. To this end, we conduct an extensive simulational scheme based on six tests of numerical precisi
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