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: Advanced Network Technologies and Intelligent Computing; Third International Anshul Verma,Pradeepika Verma,Isaac Woungang Conference proce

[復制鏈接]
樓主: 故障
11#
發(fā)表于 2025-3-23 10:21:48 | 只看該作者
12#
發(fā)表于 2025-3-23 17:20:37 | 只看該作者
Strukturpr?gende Gestaltungsprinzipien on improving the traffic sign areas in tough photos. Our technique is tested using the GTSRB dataset, which features traffic recordings collected under various CCs. Using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, the reported technique attained an accuracy of 98.62 In addition,
13#
發(fā)表于 2025-3-23 22:00:34 | 只看該作者
https://doi.org/10.1007/978-3-662-07594-4thm inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. The article presents the results of calculations for sample data sets and discusses issues related to the properties of the defined algorithm, which c
14#
發(fā)表于 2025-3-24 01:55:17 | 只看該作者
15#
發(fā)表于 2025-3-24 06:22:52 | 只看該作者
Enhanced Residual Network Framework for Robust Classification of Noisy Lung Cancer CT Images this effort will methodically assess different filtering techniques throughout a range of noise densities, from 5% to 50%. The goal of this effort is to identify lung cancer by using machine learning techniques on CT scan pictures, which will enable early and accurate cancer detection. The suggeste
16#
發(fā)表于 2025-3-24 10:20:20 | 只看該作者
17#
發(fā)表于 2025-3-24 12:17:23 | 只看該作者
18#
發(fā)表于 2025-3-24 17:48:23 | 只看該作者
Detection of?Lung Diseases Using Deep Transfer Learning-Based Convolution Neural Networksur models, the Detection performance of MobileNet and ResNet18 is quite encouraging compared to DenseNet121 and GoogLeNet. This approach could revolutionize the early detection and treatment of lung diseases, thereby enhancing patient outcomes and healthcare efficiency by providing insights into the
19#
發(fā)表于 2025-3-24 22:58:19 | 只看該作者
DG-GAN: A Deep Neural Network for?Real-World Anomaly Detection in?Surveillance Videosective functions. To address these challenges, we present a novel approach called the Dual Generator-based Generative Adversarial Network (DG-GAN). This network comprises two distinct components: a temporal generator and an image generator. The former accepts a single input in the form of a latent v
20#
發(fā)表于 2025-3-25 01:51:31 | 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 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 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 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, 2025-10-14 07:19
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 返回頂部 返回列表
南开区| 江都市| 外汇| 凌源市| 上饶县| 肥西县| 新密市| 唐山市| 九龙坡区| 朝阳县| 洪湖市| 灵璧县| 禄丰县| 成武县| 长兴县| 新营市| 辉县市| 军事| 启东市| 宜兴市| 眉山市| 道真| 大足县| 图片| 全州县| 耿马| 和龙市| 丹凤县| 奉贤区| 大洼县| 长寿区| 汨罗市| 礼泉县| 房产| 清水河县| 夹江县| 油尖旺区| 上杭县| 正安县| 谢通门县| 肃南|