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Titlebook: Computational and Statistical Methods in Intelligent Systems; Radek Silhavy,Petr Silhavy,Zdenka Prokopova Conference proceedings 2019 Spri

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發(fā)表于 2025-3-21 18:11:18 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational and Statistical Methods in Intelligent Systems
編輯Radek Silhavy,Petr Silhavy,Zdenka Prokopova
視頻videohttp://file.papertrans.cn/234/233258/233258.mp4
概述Presents the latest research on Computational and Statistical Methods in Intelligent Systems.Gathers the Proceedings of the Computational Methods in Systems and Software 2018 (CoMeSySo 2018) conferenc
叢書名稱Advances in Intelligent Systems and Computing
圖書封面Titlebook: Computational and Statistical Methods in Intelligent Systems;  Radek Silhavy,Petr Silhavy,Zdenka Prokopova Conference proceedings 2019 Spri
描述.This book presents real-world problems and pioneering research in computational statistics, mathematical modeling, artificial intelligence and software engineering in the context of intelligent systems. It gathers the peer-reviewed proceedings of the 2nd Computational Methods in Systems and Software 2018 (CoMeSySo 2018), a conference that broke down traditional barriers by being held online. The goal of the event was to provide an international forum for discussing the latest high-quality research results..
出版日期Conference proceedings 2019
關(guān)鍵詞Intelligent Systems; Computational Methods in Systems; Compuational Methods in Software; CoMeSySo; CoMeS
版次1
doihttps://doi.org/10.1007/978-3-030-00211-4
isbn_softcover978-3-030-00210-7
isbn_ebook978-3-030-00211-4Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Advances in Intelligent Systems and Computinghttp://image.papertrans.cn/c/image/233258.jpg
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https://doi.org/10.1007/978-3-030-47091-3 to construct a complete ubiquitous environment, but still it is not meant for offering massive connectivity with large number of heterogeneous sensors. This problem gives rise to scalability issue that is yet an open-end problem. Therefore, this paper introduces a unique approach of improving the a
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https://doi.org/10.1007/978-3-030-47091-3achine learning methods are helpful in software defect prediction, even though with the challenge of imbalanced software defect distribution, such that the non-defect modules are much higher than defective modules. In this paper we introduce an enhancement for the most resent hybrid SMOTE-Ensemble a
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https://doi.org/10.1007/978-3-030-47091-3ree element based steering antenna can offer the significant improvement in the performance with respect to bit error rate (BER) and provides the extra diversity gain without altering the radio frequency (RF) of front end circuits. This system uses a channel estimator i.e., mean square error (MSer)
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Synchronization in Time-Varying Networks,y methods and techniques, which are of a low efficiency in conditions of theme uncertainty..Within this paper, a novel approach to the potentially hazardous text identification under theme uncertainty is presented. The main idea of data processing approach proposed is based on the user and automatic
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Chains of Coupled Limit-Cycle Oscillatorsch data. In favor of these applications or for purposes of a reduction of a computational complexity, modifications of established approaches can be advantageous. In this paper, a statistical approach to practical providing hypotheses testing with two categorical variables is simplified. This presen
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