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Papers by Khaoula ben abdellafou
Computer Systems Science and Engineering
Proceedings of the 10th WSEAS …, 2010
This paper proposes a comparative study of three identification kernel methods of nonlinear syste... more This paper proposes a comparative study of three identification kernel methods of nonlinear systems modelled in Reproducing Kernel Hilbert Space (RKHS), where the model output results from a linear combination of kernel functions. The coefficients of this ...
Journal of Ambient Intelligence and Humanized Computing, May 25, 2022
Concurrency and Computation: Practice and Experience
2013 International Conference on Control, Decision and Information Technologies (CoDIT), 2013
ABSTRACT
Neural Computing and Applications
Mathematical Problems in Engineering, 2022
Anomaly detection is very important for system monitoring and security since successful execution... more Anomaly detection is very important for system monitoring and security since successful execution of these engineering tasks depends on access to validated data. The localization of the variable causing the fault is very essential. Indeed, the localization of the fault is defined as the ability to determine the source of the fault on a system. Generally, the identification of faults is linked to the detection procedure implemented. Therefore, it is very important to choose the adequate fault detection model to locate fault. For nonlinear uncertain systems, the most performed fault detection method is reduced rank interval kernel principal component analysis (RRIKPCA), which enhances the computational skill by downgrading the kernel matrix dimension. We have proposed in this article a new fault localization technique for uncertain systems, named partial RRIKPCA, which combines the benefits of the RRIKPCA technique and the principle of partial localization. The principal of this metho...
J. Intell. Fuzzy Syst., 2021
The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in ... more The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in the past decade has led to a remarkable advance in biomedical applications and devices. Particularly, CAM and CAD systems are employed in medical engineering, robotic surgery, clinical medicine, dentistry and other biomedical areas. Hence, the accuracy and precision of the CAD and CAM systems are extremely important for proper treatment. This work suggests a new CAD system for brain image classification by analyzing Magnetic Resonance Images (MRIs) of the brain. Firstly, we use the proposed Downsized Rank Kernel Partial Least Squares (DR-KPLS) as a feature extraction technique. Then, we perform the classification using Support Vector Machines (SVM) and we validate with a k-fold cross validation approach. Further, we utilize the Tabu search metaheuristic approach in order to determine the optimal parameter of the kernel function. The proposed algorithm is entitled DR-KPLS+SVM. The algorit...
Transactions of the Institute of Measurement and Control, 2018
Process monitoring is an integral part of chemical process, required higher product quality and s... more Process monitoring is an integral part of chemical process, required higher product quality and safety operation. Therefore, the objective of this paper is to ensure the suitable functioning and to improve the fault detection performance of conventional kernel Principal Components Analysis (KPCA). Thus, an online Reduced Rank KPCA (OnRR-KPCA) with adaptive model has been developed to monitor a dynamic nonlinear process. The developed method is proposed. Firstly, to extract the useful observations, from large amount of training data registered in normal operating conditions, in order to construct the reduced reference model. Secondly, to monitor the process online and update the reference model if a new useful observation is available and satisfies the condition of independencies between variables in feature space. To demonstrate the effectiveness of the OnRR-KPCA with adaptive model over the conventional KPCA and the RR-KPCA, the fault detection performances are illustrated through ...
Advances in Intelligent Systems and Computing, 2017
This paper considers the problem of scheduling n tasks subject to intree-precedence constraints o... more This paper considers the problem of scheduling n tasks subject to intree-precedence constraints on m identical machines under non-availability constraints. The objective is to minimize the makespan. This problem is known to be NP-hard. We propose and test several heuristic variants based on different selection and dispatching rules.
Applied Mathematical Modelling, 2013
A mathematical programming model is proposed for the two parallel machines scheduling problem whe... more A mathematical programming model is proposed for the two parallel machines scheduling problem where one machine is periodically unavailable, jobs are non-preemptive, and the objective is minimizing the makespan. The model is established by transforming the two parallel machine setting into a single machine setting. Average-case analyses of the classical Longest Processing Time first (LPT) algorithm and the List Scheduling (LS) are presented. Computational experiments show that the LPT algorithm beats the LS algorithm in all the 96 combinations of two main parameters from an average-case error point of view and that the average-case error of the LPT algorithm is less than 2% when the number of jobs is greater than twenty. Unexpectedly, there also exist instances showing that the LS algorithm may beat the LPT algorithm from the average-case error point of view.
The Computer Journal
Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of... more Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper, to reduce the complexity involved in the medical images and to ameliorate the classification of MRIs, a novel 3D magnetic resonance (MR) brain image classifier using kernel principal component analysis (KPCA) and support vector machines (SVMs) is proposed. Experiments are carried out using A deep multiple kernel SVM (DMK-SVM) and a regular SVM. An algorithm entitled SVM–KPCA is put forward. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Secondly, KPCA is applied to decrease the dimensionality of features. SVM is then applied to the reduced data. A K-fold cross-validation strategy is used to avoid overfitting and t...
Air pollution is classified as one of the most dangerous type on the human health, the environmen... more Air pollution is classified as one of the most dangerous type on the human health, the environment, and the ecosystem. However, air pollution results in climate change and affects people’s health. For a number of years, monitoring the air quality has become a very urgent and necessary topic. Moreover, safety and health have been attracting attention as one of the important topics to evaluate, firstly, the degree of air pollution and predict pollutant concentrations accurately. Then, it is crucial to establish a more scientific air quality monitoring to ensure the quality of life. In this paper, new reduced air quality monitoring is suggested to enhance the Fault Detection (FD) of an air quality monitoring network. Furthermore, a sensor FD procedure based on Reduced Kernel Partial Least Squares (RKPLS) is proposed to monitor an air quality monitoring network. The main contribution of the suggested procedure is to enhance the FD of an air quality monitoring network in terms of computa...
Air pollution has become the fourth leading cause of premature death on Earth. Air pollution caus... more Air pollution has become the fourth leading cause of premature death on Earth. Air pollution causes poor health and death; about one case out of every ten deaths worldwide is caused by air pollution, which is six times more than malaria. Human activities are the main cause of air pollution, such as chemical industries, road traffic, and fossil fuel power plants. Over the span of several years, monitoring air quality has become an exigent and essential task. In order to limit the health impact of air pollution and to ensure safe operation of chemical processes, it is necessary to quickly detect and locate instrumentation defects. There are several process monitoring techniques in the literature. Among these techniques is the one selected for this work for the detection and location of sensor faults: the kernel principal component analysis (KPCA) method, which was selected for its primary advantages of easy employment and less necessity for prior knowledge. Using the KPCA method for m...
The International Journal of Advanced Manufacturing Technology
International Journal of Imaging Systems and Technology
Arabian Journal for Science and Engineering
The International Journal of Advanced Manufacturing Technology
Computer Systems Science and Engineering
Proceedings of the 10th WSEAS …, 2010
This paper proposes a comparative study of three identification kernel methods of nonlinear syste... more This paper proposes a comparative study of three identification kernel methods of nonlinear systems modelled in Reproducing Kernel Hilbert Space (RKHS), where the model output results from a linear combination of kernel functions. The coefficients of this ...
Journal of Ambient Intelligence and Humanized Computing, May 25, 2022
Concurrency and Computation: Practice and Experience
2013 International Conference on Control, Decision and Information Technologies (CoDIT), 2013
ABSTRACT
Neural Computing and Applications
Mathematical Problems in Engineering, 2022
Anomaly detection is very important for system monitoring and security since successful execution... more Anomaly detection is very important for system monitoring and security since successful execution of these engineering tasks depends on access to validated data. The localization of the variable causing the fault is very essential. Indeed, the localization of the fault is defined as the ability to determine the source of the fault on a system. Generally, the identification of faults is linked to the detection procedure implemented. Therefore, it is very important to choose the adequate fault detection model to locate fault. For nonlinear uncertain systems, the most performed fault detection method is reduced rank interval kernel principal component analysis (RRIKPCA), which enhances the computational skill by downgrading the kernel matrix dimension. We have proposed in this article a new fault localization technique for uncertain systems, named partial RRIKPCA, which combines the benefits of the RRIKPCA technique and the principle of partial localization. The principal of this metho...
J. Intell. Fuzzy Syst., 2021
The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in ... more The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in the past decade has led to a remarkable advance in biomedical applications and devices. Particularly, CAM and CAD systems are employed in medical engineering, robotic surgery, clinical medicine, dentistry and other biomedical areas. Hence, the accuracy and precision of the CAD and CAM systems are extremely important for proper treatment. This work suggests a new CAD system for brain image classification by analyzing Magnetic Resonance Images (MRIs) of the brain. Firstly, we use the proposed Downsized Rank Kernel Partial Least Squares (DR-KPLS) as a feature extraction technique. Then, we perform the classification using Support Vector Machines (SVM) and we validate with a k-fold cross validation approach. Further, we utilize the Tabu search metaheuristic approach in order to determine the optimal parameter of the kernel function. The proposed algorithm is entitled DR-KPLS+SVM. The algorit...
Transactions of the Institute of Measurement and Control, 2018
Process monitoring is an integral part of chemical process, required higher product quality and s... more Process monitoring is an integral part of chemical process, required higher product quality and safety operation. Therefore, the objective of this paper is to ensure the suitable functioning and to improve the fault detection performance of conventional kernel Principal Components Analysis (KPCA). Thus, an online Reduced Rank KPCA (OnRR-KPCA) with adaptive model has been developed to monitor a dynamic nonlinear process. The developed method is proposed. Firstly, to extract the useful observations, from large amount of training data registered in normal operating conditions, in order to construct the reduced reference model. Secondly, to monitor the process online and update the reference model if a new useful observation is available and satisfies the condition of independencies between variables in feature space. To demonstrate the effectiveness of the OnRR-KPCA with adaptive model over the conventional KPCA and the RR-KPCA, the fault detection performances are illustrated through ...
Advances in Intelligent Systems and Computing, 2017
This paper considers the problem of scheduling n tasks subject to intree-precedence constraints o... more This paper considers the problem of scheduling n tasks subject to intree-precedence constraints on m identical machines under non-availability constraints. The objective is to minimize the makespan. This problem is known to be NP-hard. We propose and test several heuristic variants based on different selection and dispatching rules.
Applied Mathematical Modelling, 2013
A mathematical programming model is proposed for the two parallel machines scheduling problem whe... more A mathematical programming model is proposed for the two parallel machines scheduling problem where one machine is periodically unavailable, jobs are non-preemptive, and the objective is minimizing the makespan. The model is established by transforming the two parallel machine setting into a single machine setting. Average-case analyses of the classical Longest Processing Time first (LPT) algorithm and the List Scheduling (LS) are presented. Computational experiments show that the LPT algorithm beats the LS algorithm in all the 96 combinations of two main parameters from an average-case error point of view and that the average-case error of the LPT algorithm is less than 2% when the number of jobs is greater than twenty. Unexpectedly, there also exist instances showing that the LS algorithm may beat the LPT algorithm from the average-case error point of view.
The Computer Journal
Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of... more Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper, to reduce the complexity involved in the medical images and to ameliorate the classification of MRIs, a novel 3D magnetic resonance (MR) brain image classifier using kernel principal component analysis (KPCA) and support vector machines (SVMs) is proposed. Experiments are carried out using A deep multiple kernel SVM (DMK-SVM) and a regular SVM. An algorithm entitled SVM–KPCA is put forward. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Secondly, KPCA is applied to decrease the dimensionality of features. SVM is then applied to the reduced data. A K-fold cross-validation strategy is used to avoid overfitting and t...
Air pollution is classified as one of the most dangerous type on the human health, the environmen... more Air pollution is classified as one of the most dangerous type on the human health, the environment, and the ecosystem. However, air pollution results in climate change and affects people’s health. For a number of years, monitoring the air quality has become a very urgent and necessary topic. Moreover, safety and health have been attracting attention as one of the important topics to evaluate, firstly, the degree of air pollution and predict pollutant concentrations accurately. Then, it is crucial to establish a more scientific air quality monitoring to ensure the quality of life. In this paper, new reduced air quality monitoring is suggested to enhance the Fault Detection (FD) of an air quality monitoring network. Furthermore, a sensor FD procedure based on Reduced Kernel Partial Least Squares (RKPLS) is proposed to monitor an air quality monitoring network. The main contribution of the suggested procedure is to enhance the FD of an air quality monitoring network in terms of computa...
Air pollution has become the fourth leading cause of premature death on Earth. Air pollution caus... more Air pollution has become the fourth leading cause of premature death on Earth. Air pollution causes poor health and death; about one case out of every ten deaths worldwide is caused by air pollution, which is six times more than malaria. Human activities are the main cause of air pollution, such as chemical industries, road traffic, and fossil fuel power plants. Over the span of several years, monitoring air quality has become an exigent and essential task. In order to limit the health impact of air pollution and to ensure safe operation of chemical processes, it is necessary to quickly detect and locate instrumentation defects. There are several process monitoring techniques in the literature. Among these techniques is the one selected for this work for the detection and location of sensor faults: the kernel principal component analysis (KPCA) method, which was selected for its primary advantages of easy employment and less necessity for prior knowledge. Using the KPCA method for m...
The International Journal of Advanced Manufacturing Technology
International Journal of Imaging Systems and Technology
Arabian Journal for Science and Engineering
The International Journal of Advanced Manufacturing Technology