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Titlebook: Researching Mathematical Modelling Education in Disruptive Times; Hans-Stefan Siller,Vince Geiger,Gabriele Kaiser Book 2024 The Editor(s)

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書目名稱Researching Mathematical Modelling Education in Disruptive Times
編輯Hans-Stefan Siller,Vince Geiger,Gabriele Kaiser
視頻videohttp://file.papertrans.cn/829/828112/828112.mp4
概述Includes case studies describing implementation of mathematical modeling in practice.Points out directions of further innovations in mathematical modeling education.Contains high-level research of mat
叢書名稱International Perspectives on the Teaching and Learning of Mathematical Modelling
圖書封面Titlebook: Researching Mathematical Modelling Education in Disruptive Times; Hans-Stefan Siller,Vince Geiger,Gabriele Kaiser Book 2024 The Editor(s)
描述.This edited volume documents research on mathematical modelling education, before, during, and after the Covid 19 pandemic. Mathematical modelling is essential for understanding natural and human generated phenomena, and informs decision-making about events such as the pandemic,?climate change, and other disruptive events. Communication to the public, often by the media,?makes use of mathematical modelling to justify changes to public policy, as seen during the?COVID-19 crisis. Consequently, mathematical modelling has assumed an increasingly prominent role in curricula internationally, providing opportunities to understand how?it?is used in current circumstances and to plan for the needs of future societies. This book focuses on research on mathematical modelling education and its?implementation?at school and tertiary level.?Contributions to the book and point to directions for?further innovation?in mathematical modelling education...Authors of this volume are members of the International Community of Teachers of Mathematical Modelling, the peak research body for the teaching and learning of mathematical modelling..
出版日期Book 2024
關(guān)鍵詞International research on mathematical modelling; Technology and mathematical modelling education; Tea
版次1
doihttps://doi.org/10.1007/978-3-031-53322-8
isbn_softcover978-3-031-53324-2
isbn_ebook978-3-031-53322-8Series ISSN 2211-4920 Series E-ISSN 2211-4939
issn_series 2211-4920
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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classifier in dealing with uncertain data, we propose a new possibilistic evolutionary detection method, named ADIPOK (Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs), that is able to deal with label uncertainty using some concepts stemming from the Possibility theory
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ze and power consumption. The most suitable approximate multipliers are automatically selected from a library of approximate multipliers. Evolved CNNs are compared with common human-created CNNs of a similar complexity on the CIFAR-10 benchmark problem.
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Vince Geiger thus obtaining a tighter bound for .[.(., . + 1)]. This bound is a function of the selection probabilities of the schema itself and of a set of lower-order schemata which one-point crossover uses to build instances of the schema. This result supports the existence of building blocks in GP which, ho
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Xiaoli Lu,Huimin Maor classification with missing data that uses genetic programming as a regression method to impute missing values. The experiments on six benchmark datasets and five popular classifiers compare GPI with five other popular and advanced regression-based imputation methods in MICE on two measures: clas
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Katrin Vorh?lteric Programming (MLGP) approach, which has never been applied to JSS problems. Second, we extend an existing approach for a static JSS problem, called Ensemble Genetic Programming for Job Shop Scheduling (EGP-JSS), by adding “l(fā)ess-myopic” terminals that take job and machine attributes outside of the
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