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Titlebook: Computer Information Systems and Industrial Management; 20th International C Khalid Saeed,Ji?í Dvorsky Conference proceedings 2021 Springer

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31#
發(fā)表于 2025-3-27 00:18:14 | 只看該作者
32#
發(fā)表于 2025-3-27 03:56:59 | 只看該作者
https://doi.org/10.1007/978-3-8348-9093-1employed various evolutionary algorithms, and most of them employed Genetic Algorithm (GA). The interactive type of GA is called IGA, and the IGA is applied to create user’s media content such as computer graphics, music, and sound. As a special application of the IGA, the creation of the scent was
33#
發(fā)表于 2025-3-27 06:29:23 | 只看該作者
https://doi.org/10.1007/978-3-8348-9093-1design with a combination of Bayesian optimization (BO) and StyleGAN2. To estimate a facial attractiveness function from pairwise comparison data, we used a BO that incorporates Gaussian process preference learning (GPPL). Fifty female Japanese university students provided facial photographs. We emb
34#
發(fā)表于 2025-3-27 10:52:53 | 只看該作者
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發(fā)表于 2025-3-27 14:47:10 | 只看該作者
978-3-030-84339-7Springer Nature Switzerland AG 2021
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發(fā)表于 2025-3-27 17:57:35 | 只看該作者
37#
發(fā)表于 2025-3-28 01:00:14 | 只看該作者
Typing Pattern Analysis for Fake Profile Detection in Social Mediacked by fake users or not in the middle of the session. The objective of proposed work is to demonstrate the hypothesis that user recognition algorithms applied to raw data can perform better if requirement for feature extraction can be avoided, which in turn can remove the problem of inappropriate
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發(fā)表于 2025-3-28 05:50:25 | 只看該作者
39#
發(fā)表于 2025-3-28 09:33:56 | 只看該作者
Design of Web Application with Dynamic Generation of Forms for Group Decision-Makingbed tool to support group-decision making are the ability to generate individual matrices for each expert and subsequent generation of aggregated group-decision matrix. These two types of matrices together with the completed data are stored as two components of the problem to be solved and can be re
40#
發(fā)表于 2025-3-28 12:57:05 | 只看該作者
Neural Networks as Tool to Improve the Intrusion Detection Systemizing maps (SOM and GHSOM), with the purpose of classifying bi-class network traffic automatically. From the above, a 97.09% hit rate was obtained with 21 features by implementing the GHSOM classifier with 10-fold cross-validation with the ONE R feature selection technique, which would improve the e
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