Titlebook: Lessing-Handbuch; Leben – Werk – Wirku Monika Fick Book 2016Latest edition Springer-Verlag Berlin Heidelberg 2016 Gotthold Ephraim Lessing.

只看該作者 · 發(fā)表于 2025-3-21 17:15:25

書(shū)目名稱Lessing-Handbuch影響因子(影響力)

書(shū)目名稱Lessing-Handbuch影響因子(影響力)學(xué)科排名

書(shū)目名稱Lessing-Handbuch網(wǎng)絡(luò)公開(kāi)度

書(shū)目名稱Lessing-Handbuch網(wǎng)絡(luò)公開(kāi)度學(xué)科排名

書(shū)目名稱Lessing-Handbuch被引頻次

書(shū)目名稱Lessing-Handbuch被引頻次學(xué)科排名

書(shū)目名稱Lessing-Handbuch年度引用

書(shū)目名稱Lessing-Handbuch年度引用學(xué)科排名

書(shū)目名稱Lessing-Handbuch讀者反饋

書(shū)目名稱Lessing-Handbuch讀者反饋學(xué)科排名

只看該作者 · 發(fā)表于 2025-3-21 20:17:23

oduced to make up for this shortcoming. In this paper, we introduce a DL-based KT model referred to as Convolutional Attention Knowledge Tracing (CAKT) utilizing attention mechanism to augment Convolutional Neural Network (CNN) in order to enhance the ability of modeling longer range dependencies.

只看該作者 · 發(fā)表于 2025-3-22 04:09:13

Monika FickIn this paper, we presented a sentence preprocessing method that extracts the leftmost longest common sequence to obtain the common and difference subsequences between the rumor text and its explanation text to compose samples and train a supervised model for classification between rumors and explan

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只看該作者 · 發(fā)表于 2025-3-22 10:58:18

Monika Fickal results conducted in this paper compare the accuracy, efficiency, and stability of various algorithms using synthetic datasets, Sushi datasets, and Irish datasets, which demonstrate the effectiveness of our proposed algorithm in real-world scenarios.

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只看該作者 · 發(fā)表于 2025-3-22 22:01:51

Monika Fickent. Additionally, it proposes an improved Temporal Convolutional Network (TCN) named Temporal Convolutional Sparse Multilayer Perceptron Network (TCSMN). This network captures sequential structural features of cells and their surrounding neighbors, enhancing the ability to extract semantic features

只看該作者 · 發(fā)表于 2025-3-23 03:15:14

只看該作者 · 發(fā)表于 2025-3-23 05:41:48

Monika Fickwith tournament selection. Furthermore, power-law selection outperforms UMDA and the (1+1) EA in our experiments on the .-. and . k-. problems, but yields to the .-selection, tournament selection, and the self-adaptive MOSA-EA. On the unicost set cover problems, the EA with power-law selection shows

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