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Titlebook: Applied Soft Computing and Communication Networks; Proceedings of ACN 2 Sabu M. Thampi,Jaime Lloret Mauri,Axel Sikora Conference proceeding

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21#
發(fā)表于 2025-3-25 03:46:33 | 只看該作者
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發(fā)表于 2025-3-25 11:24:56 | 只看該作者
2367-3370 were carefully reviewed and selected from several initial submissions.? The book is directed to the researchers and scientists engaged in various fields of intelligent systems..978-981-33-6172-0978-981-33-6173-7Series ISSN 2367-3370 Series E-ISSN 2367-3389
23#
發(fā)表于 2025-3-25 14:30:29 | 只看該作者
Conference proceedings 20210) held in VIT, Chennai, India, during October 14–17, 2020. The research papers presented were carefully reviewed and selected from several initial submissions.? The book is directed to the researchers and scientists engaged in various fields of intelligent systems..
24#
發(fā)表于 2025-3-25 16:28:59 | 只看該作者
25#
發(fā)表于 2025-3-25 22:39:20 | 只看該作者
C. León,R. León,F. Vázquez-Poloy, precision, recall, F1 and prediction time. Experimental results of the model built demonstrated 99.92667% accuracy, 0.22 s predicting time in CICDDoS2019 and 96.2% accuracy, 0.12 s predicting time in UNSW-NB15 dataset in the instance of Random Forest classifier which is higher than other classification algorithms.
26#
發(fā)表于 2025-3-26 00:18:21 | 只看該作者
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發(fā)表于 2025-3-26 05:48:00 | 只看該作者
28#
發(fā)表于 2025-3-26 11:02:11 | 只看該作者
Distributed Denial of Service (DDoS) Attacks Detection: A Machine Learning Approach,y, precision, recall, F1 and prediction time. Experimental results of the model built demonstrated 99.92667% accuracy, 0.22 s predicting time in CICDDoS2019 and 96.2% accuracy, 0.12 s predicting time in UNSW-NB15 dataset in the instance of Random Forest classifier which is higher than other classification algorithms.
29#
發(fā)表于 2025-3-26 12:51:30 | 只看該作者
30#
發(fā)表于 2025-3-26 18:33:59 | 只看該作者
https://doi.org/10.1007/978-3-030-62669-3nd the DEAP dataset is used for other emotions. A depressed individual can get out of his sad mental state with the aid of this system. Angry, excited, pleasant and sad emotions are classified with the aid of Support Vector Machine and respective soothing videos for each emotion are played in the virtual world.
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