找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Big Data; 11th CCF Conference, Enhong Chen,Yang Gao,Wanqi Yang Conference proceedings 2023 The Editor(s) (if applicable) and The Author(s),

[復(fù)制鏈接]
樓主: 討論小組
41#
發(fā)表于 2025-3-28 16:09:55 | 只看該作者
https://doi.org/10.1057/9781137491121iew Graph Transformer module, and a multi-view attention module, which can explore the complementarity, consistency, and semantic relevance of multiple different views in online social networks. Experimental results show that MV-GT outperforms many existing methods and also demonstrates the effectiv
42#
發(fā)表于 2025-3-28 19:53:22 | 只看該作者
43#
發(fā)表于 2025-3-29 00:44:39 | 只看該作者
Female Genital Mutilation/-Cutting the relative temporal distance between power consumption data points and their neighbors. We validate the proposed model using the SGCC dataset, and our experimental results demonstrate high accuracy, precision, F1-score, and AUC values.
44#
發(fā)表于 2025-3-29 03:25:04 | 只看該作者
45#
發(fā)表于 2025-3-29 09:06:49 | 只看該作者
Sara K. Howe,Antonnet Renae Johnsonon their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramaticall
46#
發(fā)表于 2025-3-29 11:33:00 | 只看該作者
Sara K. Howe,Antonnet Renae Johnsontiple individual models have demonstrated promising results for forecasting performance. However, these models also face the issues of high computational cost and time consumption when dealing with multiple time-series. To address these issues, this paper proposes a novel framework that integrates m
47#
發(fā)表于 2025-3-29 16:12:52 | 只看該作者
48#
發(fā)表于 2025-3-29 20:36:09 | 只看該作者
49#
發(fā)表于 2025-3-30 01:45:13 | 只看該作者
Sara K. Howe,Antonnet Renae Johnsonnly focus on extracting information from high-level features, while ignoring the influence of low-level features on FGVC. Based on this, this paper integrates low-level detailed information and high-level semantic information to improve the model performance by enhancing the feature representation a
50#
發(fā)表于 2025-3-30 05:49:01 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-8 08:53
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
甘德县| 化德县| 全椒县| 望江县| 怀柔区| 永济市| 西充县| 雷山县| 祥云县| 嘉兴市| 石台县| 柳河县| 雷山县| 海丰县| 湘潭县| 墨玉县| 沅陵县| 保亭| 顺昌县| 托里县| 和龙市| 潢川县| 通山县| 昌江| 麻栗坡县| 德庆县| 都匀市| 亚东县| 集安市| 呼玛县| 寿宁县| 乾安县| 怀来县| 肥乡县| 大邑县| 富川| 永平县| 双城市| 江北区| 固安县| 淄博市|