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: Deep Learning Theory and Applications; 4th International Co Donatello Conte,Ana Fred,Carlo Sansone Conference proceedings 2023 The Editor(s

[復(fù)制鏈接]
樓主: 口語
31#
發(fā)表于 2025-3-26 21:54:22 | 只看該作者
32#
發(fā)表于 2025-3-27 03:23:36 | 只看該作者
,Synthetic Network Traffic Data Generation and?Classification of?Advanced Persistent Threat Samples:metrics indicate successful generation and detection with an accuracy of 99.97% a recall rate of 99.94%, and 100% precision. Further results show a 99.97% . score for detecting APT samples in the synthetic data, and a Receiver Operator Characteristic Area Under the Curve (ROC_AUC) value of 1.0, indi
33#
發(fā)表于 2025-3-27 07:39:39 | 只看該作者
34#
發(fā)表于 2025-3-27 12:59:16 | 只看該作者
35#
發(fā)表于 2025-3-27 15:35:40 | 只看該作者
,Research Data Reusability with?Content-Based Recommender System,te that the developed prototype content-based recommender system effectively provides relevant recommendations for research data repositories. The evaluation of the system using standard evaluation metrics shows that the system achieves an accuracy of 79% in recommending relevant items. Additionally
36#
發(fā)表于 2025-3-27 20:54:04 | 只看該作者
,MSDeepNet: A Novel Multi-stream Deep Neural Network for?Real-World Anomaly Detection in?Surveillanction module (WS-TAM). The features extracted from the individual streams are fed to train the modified MIL classifier by employing a novel temporal loss function. Finally, a fuzzy fusion method is used to aggregate the anomaly detection scores. To validate the performance of the proposed method, com
37#
發(fā)表于 2025-3-28 00:31:55 | 只看該作者
,Explaining Relation Classification Models with?Semantic Extents,ng both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the
38#
發(fā)表于 2025-3-28 05:16:48 | 只看該作者
39#
發(fā)表于 2025-3-28 08:31:17 | 只看該作者
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can
40#
發(fā)表于 2025-3-28 12:48:13 | 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(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-24 07:26
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
曲麻莱县| 黑河市| 浙江省| 兴文县| 石棉县| 昌图县| 普定县| 岳普湖县| 绥中县| 灯塔市| 芜湖县| 淳安县| 扎鲁特旗| 阿坝| 乐亭县| 积石山| 安塞县| 玉门市| 印江| 子长县| 双柏县| 广州市| 绩溪县| 于田县| 衡阳县| 永吉县| 郁南县| 涿鹿县| 朝阳区| 长沙市| 特克斯县| 上蔡县| 普洱| 神池县| 汝南县| 临沂市| 桐梓县| 酒泉市| 册亨县| 西盟| 甘肃省|