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
绥宁县| 屯门区| 磐安县| 紫阳县| 区。| 新沂市| 云林县| 南召县| 五寨县| 德江县| 西乡县| 蓬莱市| 海原县| 来安县| 遵义县| 德格县| 阳泉市| 建昌县| 宾川县| 景洪市| 前郭尔| 益阳市| 浙江省| 鹤岗市| 沂水县| 宁明县| 璧山县| 华安县| 深泽县| 堆龙德庆县| 涡阳县| 永宁县| 醴陵市| 虞城县| 仲巴县| 隆尧县| 图们市| 临漳县| 东至县| 郓城县| 恩平市|