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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/10.1007/978-3-031-63772-8
isbn_softcover978-3-031-63771-1
isbn_ebook978-3-031-63772-8Series 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




书目名称Computational Science – ICCS 2024网络公开度学科排名




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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Rational Functions and Rational Mapsworks. Building on the methodology of a previous proposal (GENECI), this research adds a local search phase that incorporates prior knowledge about gene interactions, thereby enhancing the optimization process under the influence of domain expert. The algorithm focuses on the evaluation of candidate
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Grassmannians and Related Varietiesy of non-linear dynamical systems. The correlation dimension and sample entropy are computed for time series representing angles of skeletal body joints as well as whole-body orientation and translation. Two aggregation strategies for pose parameters are used – averaging of Euler angles triplets and
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https://doi.org/10.1007/978-3-642-57878-6auses both motor and non-motor symptoms. Understanding the neuronal processes that underlie PD is critical for creating successful therapies. This work presents a novel strategy that combines machine learning (ML) and stochastic modelling with connectomic data to understand better the complicated br
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Advanced Lectures in Mathematicsdical context, the negation detection plays a pivotal role in understanding clinical documentation and extracting meaningful insights. In this paper, we survey 16 articles published from 2005 to 2023 and focusing on negation detection within medical domain. Our evaluation framework encompass both me
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https://doi.org/10.1007/978-3-8348-9722-0rom biological databases (DB), is important for the accelerated development of molecular diagnostics and therapy..In this study, we present . user-friendly R toolkit (R package and Shiny web application) for heterogeneous ensemble feature selection (EFS) of molecular omics data that also supports us
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