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| 1 | Stability for Ekeland'sε-vail ational Principle and Cone Extremal Solutions显示文摘 | ATTOUCH H RIAHI H | 1993 | Mathe matics of Operations Resereh1993，18，： | 1 |
| 2 | Familles d'operators maximaux monotones et mesurabilite显示文摘 | Attouch H | 1979 | Annalidi Mathematica Puraed Applicata1979，，： | 1 |
| 3 | Stability for Ekeland's ε-variational principle and cone extremal solutions显示文摘 | Attouch H Riahi H | 1993 | Journal of Mathematics of Operations Research1993，18，： | 1 |
| 4 | Asymptotic Normality of a robust estimator of the regression function for functional time series data显示文摘 | Attouch M Laksaci A Ould-Sa(i)d E | | 0，，04： | 1 |
| 5 | Stability for Ekeland's-variational Principle and Cone Extremal Solutions显示文摘 | Attouch H Riahi H | 1993 | Mathematics of Operations Reserch1993，18，： | 1 |
| 6 | Convergence of descent methods for semi-algebraic and tame problems : proximal algorithms,forward-backward splitting, and regularized Gauss-Seidel methods 显示文摘 | ATTOUCH H B0LTE J SVAITER B F | 2013 | Mathematical Programming2013，137，1： | 1 |
| 7 | Viscosity approximation methods for minimization problems显示文摘 | ATTOUCH H | 1996 | SIAM J Optim1996，6，3： | 1 |
| 8 | Convergence des points minsup et de points fixes 显示文摘 | Attouch H Wets R | 1983 | C B Acad Sci Paris1983，296，： | 1 |
| 9 | Recession operators and solvability of variational problems显示文摘 | Attouch H Chbani Z Moudafi A | 1994 | Series on Advances in Mathematices for Applied Science World Scientif1994，18，： | 1 |
| 10 | Stability of geometric Ekeland variational principle : convex case显示文摘 | Attouch H Beer G | 1994 | Optim Theory Apple1994，81，： | 1 |
| 11 | Stability results for Ekeland' s E- variational principle and cone extremal solution显示文摘 | Attouch H Riahi H | 1993 | Mathematics of Operations Research1993，18，1： | 1 |
| 12 | Stability for Ekeland's-variational principle and cone external solutions显示文摘 | ATTOUCH H RIAHI H | 1993 | Mathematics of Operations Reserch1993，18，： | 1 |
| 13 | Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods显示文摘The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food industry.The k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR data.The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables.The objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour procedures.In this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator data.Specifically,we propose three models for this kind of data which are Functional Nonparametric Regression,Functional Robust Regression,and Functional Relative Error Regression,with both kernel and k-NN approaches to compare between them.The experimental result shows the higher efficiency of k-NN predictor over the kernel predictor.The predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods. | Omar Fetitah Ibrahim M.Almanjahie Mohammed Kadi Attouch Salah Khardani | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 14 | Functional Nonparametric Predictions in Food Industry Using Near-Infrared Spectroscopy Measurement显示文摘Functional statistics is a new technique for dealing with data thatcan be viewed as curves or images. Parallel to this approach, the Near-InfraredReflectance (NIR) spectroscopymethodology has been used in modern chemistryas a rapid, low-cost, and exact means of assessing an object’s chemicalproperties. In this research, we investigate the quality of corn and cookiedough by analyzing the spectroscopic technique using certain cutting-edgestatistical models. By analyzing spectral data and applying functional modelsto it, we could predict the chemical components of corn and cookie dough.Kernel Functional Classical Estimation (KFCE), Kernel Functional QuantileEstimation (KFQE), Kernel Functional Expectile Estimation (KFEE),Semi-Partial Linear Functional Classical Estimation (SPLFCE), Semi-PartialLinear Functional Quantile Estimation (SPLFQE), and Semi-Partial LinearFunctional Expectile Estimation (SPLFEE) are models used to accuratelyestimate the different quantities present in Corn and Cookie dough. Theselection of these functional models is based on their ability to constructa forecast region with a high level of confidence. We demonstrate that theconsidered models outperform traditional models such as the partial leastsquaresregression and the principal component regression in terms of predictionaccuracy. Furthermore, we show that the proposed models are morerobust than competing models such as SPLFQE and SPLFEE in the sensethat data heterogeneity has no effect on their efficiency. | Ibrahim M.Almanjahie Omar Fetitah Mohammed Kadi Attouch Tawfik Benchikh | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 15 | The k Nearest Neighbors Estimator of the M-Regression in Functional Statistics显示文摘It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are introduced.The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic.We use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust model.Under some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach. | Ahmed Bachir Ibrahim Mufrah Almanjahie Mohammed Kadi Attouch | 2020 | Computers, Materials & Continua2020，，12： | 0 |
| 16 | Optoelectronic properties of SnO2 thin films sprayed at different deposition times显示文摘This article presents the elaboration of tin oxide(SnO_2) thin films on glass substrates by using a home-made spray pyrolysis system. Effects of film thickness on the structural, optical, and electrical film properties are investigated. The films are characterized by several techniques such as x-ray diffraction(XRD), atomic force microscopy(AFM), ultravioletvisible(UV–Vis) transmission, and four-probe point measurements, and the results suggest that the prepared films are uniform and well adherent to the substrates. X-ray diffraction(XRD) patterns show that SnO_2 film is of polycrystal with cassiterite tetragonal crystal structure and a preferential orientation along the(110) plane. The calculated grain sizes are in a range from 32.93 nm to 56.88 nm. Optical transmittance spectra of the films show that their high transparency average transmittances are greater than 65% in the visible region. The optical gaps of SnO_2 thin films are found to be in a range of 3.64 e V–3.94 e V. Figures of merit for SnO_2 thin films reveal that their maximum value is about 1.15 × 10-4-1?atλ = 550 nm. Moreover, the measured electrical resistivity at room temperature is on the order of 10-2?·cm. | Allag Abdelkrim Saad Rahmane Ouahab Abdelouahab Attouche Hafida Kouidri Nabila | 2016 | Chinese Physics B2016，25，4： | 0 |
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