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Titlebook: Computational Science – ICCS 2021; 21st International C Maciej Paszynski,Dieter Kranzlmüller,Peter M. A. S Conference proceedings 2021 Spri

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樓主: 航天飛機
11#
發(fā)表于 2025-3-23 10:46:25 | 只看該作者
12#
發(fā)表于 2025-3-23 14:31:00 | 只看該作者
13#
發(fā)表于 2025-3-23 18:41:58 | 只看該作者
14#
發(fā)表于 2025-3-24 00:00:58 | 只看該作者
A Higher-Order Adaptive Network Model to Simulate Development of and Recovery from PTSDluence of therapy on the ability of an individual to learn to control the emotional responses to the traumatic mental model was modeled. Finally, a form of second-order adaptation was covered to unblock and activate this learning ability.
15#
發(fā)表于 2025-3-24 05:20:29 | 只看該作者
MGEL: A Robust Malware Encrypted Traffic Detection Method Based on?Ensemble Learning with Multi-grai we introduce the self-attention mechanism to process sequence features and solve the problem of long-term dependence. We verify the effectiveness of the MGEL on two public datasets and the experimental results show that the MGEL approach outperforms other state-of-the-art methods in four evaluation metrics.
16#
發(fā)表于 2025-3-24 06:50:53 | 只看該作者
17#
發(fā)表于 2025-3-24 10:57:01 | 只看該作者
Explaining the ENRICH Programme Process, presents the results of the experiments validating the advantage of multi-clustering approach, ., over the traditional methods based on single-scheme clustering. The experiments focus on the overall recommendation performance including accuracy and coverage as well as a cold-start problem.
18#
發(fā)表于 2025-3-24 14:55:45 | 只看該作者
19#
發(fā)表于 2025-3-24 21:43:30 | 只看該作者
Recurrent Autoencoder with?Sequence-Aware Encodingoutperforms a standard RAE in terms of model training time in most cases. The extensive experiments performed on a dataset of generated sequences of signals shows the advantages of RAES(C). The results show that the proposed solution dominates over the standard RAE, and the training process is the order of magnitude faster.
20#
發(fā)表于 2025-3-25 02:02:40 | 只看該作者
Quality of Recommendations and?Cold-Start Problem in Recommender Systems Based on Multi-clusters presents the results of the experiments validating the advantage of multi-clustering approach, ., over the traditional methods based on single-scheme clustering. The experiments focus on the overall recommendation performance including accuracy and coverage as well as a cold-start problem.
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