维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

10篇 您的检索式：作者名="ACHHAB"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	TCF7L2 is reproducibly associated with type 2 diabetes in various ethnic groups:a global meta-analysis 显示文摘	Cauehi S El Achhab Y Choquet H	2007	J Mol Med2007，85，：	1
	2	Disease-specific health-related quality of life instruments among adults diabetic:A systematic review显示文摘	El Achhab Y Nejjari C Chikri M	2008	Diabetes Res Clin Pract2008，80，：	1
	3	TCF7L2 is reproducibly associated with type 2 diabetes in various ethnic groups:a global recta-analysis显示文摘	Cauchi S E1 Achhab Y Choquet H	2007	J Mol Med(Berl)2007，85，7：	1
	4	TCF7L2 is reproducibly associated with type 2 diabetes in various ethnic groups:a global meta-analysis显示文摘	Cauchi S E1 Achhab Y Choquet H		0，，7：	1
	5	Association of the ENPP1 K121EQ polymorphism with type 2 diabetes and obesity in the Moroccan population显示文摘	ACHHAB Y E MEYRE D BOUATIA N N		0，，01：	1
	6	European genetic variants associated with type 2 diabetes in North African Arabs显示文摘	Cauchi S Ezzidi I El Achhab Y	2012	Diabetes Metab2012，38，4：	1
	7	TCF7L2 is reproducibly associated with type 2 diabetes in various ethnic groups:a global meta-analysis显示文摘	Cauchi S El Achhab Y Choquet H	2007	Mol Med2007，85，7：	1
	8	TCF7L2 is reprodueibly associated with type 2 diabetes in various ethnic groups: a global meta-analysis显示文摘	Cauchi S El Achhab Y Choquet H	2007	J Mol Med2007，85，：	1
	9	Hypertension and type 2diabetes:a crosssectional study in Morocco(EPIDIAM Study)显示文摘	BERRAHO M EL ACHHAB Y BENSLIMANE A	2012	Pan Afr Med J2012，11，：	1
	10	Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM显示文摘Remote sensing image processing engaged researchers’attentiveness in recent years,especially classification.The main problem in classification is the ratio of the correct predictions after training.Feature extraction is the foremost important step to build high-performance image classifiers.The convolution neural networks can extract images’features that significantly improve the image classifiers’accuracy.This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’outputs as a features extraction using two classic convolution models;these convolution models are the ResNet 50 and the DenseNet 169.These elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input features.The results of the proposed methods are compared with other antecedent approaches in the same experimental environments.Evaluation is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed classifiers.The proposed classifiers are used with their trained weights to predict a big remote sensing scene’s classes for a developed test.Experimental results ensure that,compared with the other traditional classifiers,the proposed classifiers are further accurate.	Khalid A.AlAfandy Hicham Omara Hala S.El-Sayed Mohammed Baz Mohamed Lazaar Osama S.Faragallah Mohammed Al Achhab	2022	Computers, Materials & Continua2022，，7：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费