FETID
发表于 2025-3-21 20:03:48
书目名称Artificial Intelligence影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0162078<br><br> <br><br>书目名称Artificial Intelligence读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0162078<br><br> <br><br>
秘传
发表于 2025-3-21 21:42:14
Image Recognition of Peanut Leaf Diseases Based on Capsule Networksase images by taking advantage of the capsule networks. Firstly, constructing the data set of the peanut leaf disease images and data enhancement was used to process the images. Secondly, this paper designed two types of capsule networks: modifying the parameters for the peanut leaf disease images a
恸哭
发表于 2025-3-22 00:48:00
http://reply.papertrans.cn/17/1621/162078/162078_3.png
共同确定为确
发表于 2025-3-22 06:22:15
TCPModel: A Short-Term Traffic Congestion Prediction Model Based on Deep Learningerm traffic speed prediction model called .. Both models are based on a deep learning method Stacked Auto Encoder (.). By comparing the other traffic flow forecasting methods and average speed forecasting methods, the methods proposed by this paper have improved the accuracy rate. For traffic conges
ostensible
发表于 2025-3-22 11:18:31
http://reply.papertrans.cn/17/1621/162078/162078_5.png
gentle
发表于 2025-3-22 14:47:52
The Methods for Reducing the Number of OOVs in Chinese-Uyghur NMT Systemnt test on low-frequency words from Chinese corpus after training and achieved an even more reduced OOV result of 98. The mass reduction of OOVs from 1.5 thousand to only a hundred signifies the effectiveness of the solutions in this study.
penance
发表于 2025-3-22 20:35:26
http://reply.papertrans.cn/17/1621/162078/162078_7.png
blight
发表于 2025-3-22 23:18:01
http://reply.papertrans.cn/17/1621/162078/162078_8.png
Matrimony
发表于 2025-3-23 04:42:18
Developing Successful Oracle Applications,t scale classification maps and obtain a final road decision map. To validate the performance of the proposed method, we test our MSCNN based method and other state-of-the-art approaches on two challenging datasets of high-resolution images. Experiments show our method gets the best results both in
流动性
发表于 2025-3-23 09:36:22
Developing Successful Oracle Applications,erm traffic speed prediction model called .. Both models are based on a deep learning method Stacked Auto Encoder (.). By comparing the other traffic flow forecasting methods and average speed forecasting methods, the methods proposed by this paper have improved the accuracy rate. For traffic conges