爆发
发表于 2025-3-21 16:08:30
书目名称Advances in Smart Medical, IoT & Artificial Intelligence影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0167314<br><br> <br><br>书目名称Advances in Smart Medical, IoT & Artificial Intelligence读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0167314<br><br> <br><br>
hereditary
发表于 2025-3-21 20:17:05
http://reply.papertrans.cn/17/1674/167314/167314_2.png
FACT
发表于 2025-3-22 02:19:56
Information Systems Engineering and Managementhttp://image.papertrans.cn/b/image/167314.jpg
Priapism
发表于 2025-3-22 04:58:41
http://reply.papertrans.cn/17/1674/167314/167314_4.png
滴注
发表于 2025-3-22 11:32:30
978-3-031-66852-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
Aura231
发表于 2025-3-22 15:26:30
http://reply.papertrans.cn/17/1674/167314/167314_6.png
Creatinine-Test
发表于 2025-3-22 18:51:09
Gruppenprozessorientierte Interventionen,tiveness in healthcare applications, is challenged by limited accessibility to substantial datasets, especially in the case of fall detection. Moreover, training deep learning models is both time-consuming and costly. To address these issues, in this paper, we implemented a sample size technique cal
可能性
发表于 2025-3-22 22:57:49
Anforderungen an die TherapeutInnen,dels to LogS (logarithmic solubility) prediction, our study systematically investigates the critical role played by diverse datasets in shaping model performance. Utilizing four distinct datasets, we explore the nuances of their impact on the predictive accuracy of linear regression models. The data
含沙射影
发表于 2025-3-23 03:40:44
http://reply.papertrans.cn/17/1674/167314/167314_9.png
blithe
发表于 2025-3-23 07:37:00
http://reply.papertrans.cn/17/1674/167314/167314_10.png