Titlebook: Computational Science – ICCS 2022; 22nd International C Derek Groen,Clélia de Mulatier,Peter M. A. Sloot Conference proceedings 2022 The Ed

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Classification Methods Based on?Fitting Logistic Regression to?Positive and?Unlabeled Datao evaluate the accuracy, the recall, the precision and the F1-score of the considered classification methods. The corresponding assessments have been carried out on 13 machine learning model schemes by conducting some numerical experiments on selected real datasets.

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Conference proceedings 2022selected from 474 submissions. 169 full and 36 short papers were accepted to the main track; 120 full and 42 short papers were accepted to the workshops/ thematic tracks. ..*The conference was held in a hybrid format.

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0302-9743 iewed and selected from 474 submissions. 169 full and 36 short papers were accepted to the main track; 120 full and 42 short papers were accepted to the workshops/ thematic tracks. ..*The conference was held in a hybrid format.978-3-031-08750-9978-3-031-08751-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Sustainable Energy for Smart Citieso evaluate the accuracy, the recall, the precision and the F1-score of the considered classification methods. The corresponding assessments have been carried out on 13 machine learning model schemes by conducting some numerical experiments on selected real datasets.

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0302-9743 Science, ICCS 2022, held in London, UK, in June 2022.*..The total of 175 full papers and 78 short papers presented in this book set were carefully reviewed and selected from 474 submissions. 169 full and 36 short papers were accepted to the main track; 120 full and 42 short papers were accepted to t

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Sustainable Energy for Smart Citiesate-of-the-art algorithms and allows to calculate bigger and more complicated games than ever before. Due to the generic and knowledge-free design of CoEvoSG, the method can be applied to diverse real-life security scenarios.

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Paula Ferreira,Elizabete Pereira oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations.