找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

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

[复制链接]
楼主: 故障
发表于 2025-3-23 10:21:48 | 显示全部楼层
发表于 2025-3-23 17:20:37 | 显示全部楼层
Strukturprägende 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,
发表于 2025-3-23 22:00:34 | 显示全部楼层
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
发表于 2025-3-24 01:55:17 | 显示全部楼层
发表于 2025-3-24 06:22:52 | 显示全部楼层
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
发表于 2025-3-24 10:20:20 | 显示全部楼层
发表于 2025-3-24 12:17:23 | 显示全部楼层
发表于 2025-3-24 17:48:23 | 显示全部楼层
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
发表于 2025-3-24 22:58:19 | 显示全部楼层
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
发表于 2025-3-25 01:51:31 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-9 20:42
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表