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Titlebook: Data Analytics in System Engineering; Proceedings of 7th C Radek Silhavy,Petr Silhavy Conference proceedings 2024 The Editor(s) (if applica

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书目名称Data Analytics in System Engineering
副标题Proceedings of 7th C
编辑Radek Silhavy,Petr Silhavy
视频video
概述Presents the latest research on Software Engineering Application in Informatics.Gathers the Proceedings of the 7th Computational Methods in Systems and Software 2023 (CoMeSySo2023) conference.Written
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Data Analytics in System Engineering; Proceedings of 7th C Radek Silhavy,Petr Silhavy Conference proceedings 2024 The Editor(s) (if applica
描述These proceedings offer an insightful exploration of integrating data analytics in system engineering. This book highlights the essential role of data in driving innovation, optimizing processes, and solving complex challenges in the field. Targeted at industry professionals, researchers, and enthusiasts, this book serves as a comprehensive resource, providing actionable insights and showcasing transformative applications of data in engineering. It is a must-read for anyone keen on understanding and participating in the ongoing evolution of system engineering in our data-centric world.
出版日期Conference proceedings 2024
关键词Software Engineering; Software Engineeering Applications; CoMeSySo; Computational Methods in Systems an
版次1
doihttps://doi.org/10.1007/978-3-031-53552-9
isbn_softcover978-3-031-53551-2
isbn_ebook978-3-031-53552-9Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
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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Use of Neighborhood-Based Bridge Node Centrality Tuple for Preferential Vaccination of Nodes to Redpreferential vaccination to reduce the spread of infections by conducting simulations of the spread of infections per the SIS (Susceptible-Infected-Susceptible) model on a collection of 10 complex real-world social networks.
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,A New Approach to Eliminating the “Flip” Effect of the Approximating Function Under Conditions of ation values are used only on the interval of the initial value, i.e. the interval where the “flip” effect was observed. The approach used for processing the measurement results makes it possible to significantly reduce the error in estimating the function of the measured process under conditions of a priori uncertainty. #COMESYSO1120.
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Applying a Recurrent Neural Network to Implement a Self-organizing Electronic Educational Course, test questions. The practical part of the study presents the results of testing the self-organizing electronic course created using recurrent neural network on a group of students. The results confirm the increase of learning efficiency and improvement of students’ results #COMESYSO1120.
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