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| 1 | Kernel k-means clustering based local support vector domain description fault detection of mul- timodal processes 显示文摘 | KHEDIRI I B WEIHS C LIMAM M | 2012 | Expert Systems with Applications2012，39，2： | 1 |
| 2 | Eficacy of dioameetite(smecta)in the treatment of acute watery diarrhoea in adults:a muhicentre,randomized,double-blind,placebo-controlled,parallel group study显示文摘 | Khediri F Mrad AI Azzouz M et a1 | 2011 | Gaatroenterol Res Praet2011，7，8： | 1 |
| 3 | Efficacy of diosmectite (smecta) in the treatment of acute watery diarrhoea in adults: a muhicentre, randomized, double-blind, placebo-controlled, parallel group study显示文摘 | Khediri F Mrad AI Azzouz M | 2011 | Gastroenterol Res Pract2011，，78： | 1 |
| 4 | Efficacy of Diosmectite (Smecta)<sup>?</sup> in the Treatment of Acute Watery Diarrhoea in Adults: A Multicentre, Randomized, Double-Blind, Placebo-Controlled, Parallel Group Study显示文摘 | Faouzi Khediri Abdennebi Ilhem Mrad Moussadek Azzouz Hedi Doughi Taoufik Najjar Hélène Mathiex-Fortunet Philippe Garnier Antoine Cortot A. Castells | 2011 | Gastroenterology Research and Practice2011，，： | 1 |
| 5 | Efficacy of dioameetite(smecta) in the treatment of acute watery diarrhorea in adults: a muhicentre, randomized, double- blind, placebo-controlled, parallel group study显示文摘 | Khediri F Mrad AI Azzouz M | 2011 | Gastroenterol Res Rraet2011，7，8： | 1 |
| 6 | Efficacy of diosmectite(smecta) in the treatment of acute watery diarrhoea in adults:a multicentre,randomized,double-blind,placebo-controlled,parallel group study显示文摘 | Khediri F Mrad AI Azzouz M | | 0，，2011： | 1 |
| 7 | Kernel k-means clustering based local support vector domain description fault detection of muhimodal processes 显示文摘 | KHEDIRI I B WEIHS C LIMAM M | 2012 | Expert Systems with Applications2012，39，2： | 1 |
| 8 | Efficacy of diosmectite (smecta) in the treatment of acute watery diarrhoea in adults:a multicentre,randomized,double-blind,placebo-controlled,parallel group study显示文摘 | Khediri F Mrad AI Azzouz M | | 0，，： | 1 |
| 9 | Variable window adaptive kernel principal component analysis for nonlinear nonstationary process monitoring 显示文摘 | Issam Ben Khediri Mohamed Limam Claus Weihs | 2011 | Computers & Industrial Engineering2011，，61： | 1 |
| 10 | Kernel K-means clusteringbased local support vector domain descrition fault detection of multi-modal processes显示文摘 | KHEDIRI I B WEIHS C LIMAM M | 2012 | Expert Systems with Applications2012，39，2： | 1 |
| 11 | Data Augmentation and Random Multi-Model Deep Learning for Data Classification显示文摘In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML models.The increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen examples.Deep learning(DL)models have a lot of parameters,and they frequently overfit.Effectively,to avoid overfitting,data plays a major role to augment the latest improvements in DL.Nevertheless,reliable data collection is a major limiting factor.Frequently,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in batches.In this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for classification.We present a methodology for using Generative Adversarial Networks(GANs)to generate images for data augmenting.Through experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency.Experimenting across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models. | Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan | 2023 | Computers, Materials & Continua2023，，3： | 0 |
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