@article {62656,
	title = {Genetic robust kernel sample selection for chemometric data analysis},
	journal = {Journal of Chemometrics},
	volume = {35},
	number = {6},
	year = {2021},
	month = {2021},
	abstract = {
	In this work, we propose a new algorithm to improve existing techniques used in the field of spectroscopic data regression analysis. In particular, it combines the power of nonlinear kernel regressors (kernel ridge regression [KRR], kernel principal component regression [KPCR], and Gaussian process regression [GPR]) with an optimization based on nondominated sorting multi-objective genetic algorithm (NSGAII) to filter the residual outliers in the prediction space and leverage points in the features space. The proposed algorithm, contrary to most existing robust algorithms, simultaneously optimizes many complementary objectives for an automatic adaptation and thus a better outliers detection. It is well known that the elimination of outliers greatly improves the regression model. It is thus the aim of this work to develop a new robust regression algorithm. It has been applied on five different datasets, and the results are compared to both classical nonlinear regression methods and the commonly used robust regression methods robust continuum regression (RCR), partial robust M-regression (PRM), robust principal component regression (RPCR), robust PLSR (RSIMPLS), and locally weighted regression (LWR). They show that the proposed algorithm outperforms the classical nonlinear regression methods and is a promising competitor to the robust methods outperforming most of them. Even though the results obtained are only from five datasets, this algorithm can be considered an interesting contribution for improving data analysis in the field of chemometrics.

},
	isbn = {0886-9383},
	url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/cem.3344},
	author = {Fouzi Douak and Ghoggali, Noureddine and Hedjam, Rachid and Mekhalfi, Mohamed Lamine and Nabil Benoudjit and Farid Melgani}
}
@article {64842,
	title = {A modular support vector machine for active learning of urban remote sensing images classification in Algeria},
	journal = {Journal of the Indian Society of Remote SensingJournal of the Indian Society of Remote Sensing},
	volume = {46},
	year = {2018},
	month = {2018},
	pages = {515-529},
	isbn = {0974-3006},
	author = {Marir, Belkacem and Mahdi KALLA and Fouzi Douak and Daamouche, Abdelhamid}
}
@proceedings {64605,
	title = {Thermal stability investigation of power GaN HEMT includingself-heating effects},
	journal = {6th International Conference on Systems and Control (ICSC)},
	year = {2017},
	month = {2017},
	address = {Batna, Algeria},
	abstract = {
	In this paper, exhaustive analytical investigation based on analyzing the impact of the self-heating phenomenon on the power GaN HEMT performance is proposed. To do so, analytical models for the drain current, power dissipation and lattice temperature variation are developed in order to evaluate the device reliability against the self-heating effects (SHEs). The transistor thermal stability is systematically investigated with respect to the dependence on the buffer layer doping, mole fraction variation, and layer thickness. In this work, the electrical and thermal performance of power GaN HEMT structure is investigated. Also, device design parameters dependent characteristics on thermal stability and immunity are observed and analyzed. The obtained results provide new insight for bridging the gap between high power performances with thermal stability factor.

},
	author = {Aouf, Anouar Essadat and Fay{\c c}al DJEFFAL and Fouzi Douak}
}
@proceedings {65493,
	title = {A wavelet optimization approach for microemboli classification using RF signals},
	year = {2016},
	month = {2016},
	pages = {1-4},
	publisher = {IEEE},
	abstract = {Wavelets are known particularly to be an effective tool for extracting discriminative features in the scattered RF signals of both solid and gaseous emboli. However, the selection of an appropriate mother wavelet for the signal being analyzed is an important criterion. This offers the possibility to perform an optimization procedure to obtain the best wavelet. The purpose of the study is to propose a new approach to classify microembolic echoes using a discrete wavelet transform (DWT) based on genetic algorithm optimization and support vector machine (SVM) classifier. The experimental setup consists of a flow phantom (ATSLaB) containing a tube of 6 mm in diameter. In order to mimic the ultrasonic behavior of gaseous emboli, contrast agents consisting of microbubbles are used in our experimental setup. However, to mimic the behavior of the solid emboli we have used the Doppler fluid which contains particles with scatter characteristics comparable to red blood cells. The acquisitions are carried out at 2 MHz and 3.5 MHz transmit frequency. Ultrasound waves are transmitted at different intensities corresponding to mechanical indices (MI) of 0.21 and 0.42 for the transmit frequency of 2 MHz, and 0.31 and 0.62 for the transmit frequency of 3.5 MHz. Two concentrations of the contrast agent (100 μl and 200 μl) are diluted into a 100 ml volume of water. The polyphase representation of the discrete wavelet transform (DWT) is exploited in this study. Such representation allows generating a wavelet filter bank from a set of angular parameters, in order to minimize the fitness function based on genetic algorithm optimization and the SVM classifier. The best accuracy classifications of microemboli obtained in this study are equal to 99.90\% for 2MHz and to 99.60\% for 3.5MHz. These results illustrate that wavelet optimization approach works well for microemboli classification using RF signals.},
	author = {Fouzi Douak and Abdelghani Tafsast and Damien Fouan and Karim Ferroudji and Ayache Bouakaz and Nabil Benoudjit}
}
@proceedings {66705,
	title = {Design of a multiblock general regression neural network for wind speed prediction in Algeria},
	journal = {2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)},
	year = {2013},
	month = {2013},
	pages = {390-395},
	publisher = {IEEE},
	isbn = {1-4673-5540-2},
	author = {Fouzi Douak and Nabil Benoudjit and Farid Melgani}
}
@article {66704,
	title = {Kernel ridge regression with active learning for wind speed prediction},
	journal = {Applied energyApplied Energy},
	volume = {103},
	year = {2013},
	month = {2013},
	pages = {328-340},
	isbn = {0306-2619},
	author = {Fouzi Douak and Farid Melgani and Nabil Benoudjit}
}
@article {67024,
	title = {Active learning for spectroscopic data regression},
	journal = {Journal of ChemometricsJournal of Chemometrics},
	volume = {26},
	year = {2012},
	month = {2012},
	pages = {374-383},
	isbn = {0886-9383},
	author = {Fouzi Douak and Farid Melgani and Alajlan, Naif and Pasolli, Edoardo and Bazi, Yakoub and Nabil Benoudjit}
}
@proceedings {67023,
	title = {SVR active learning for product quality control},
	journal = {2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)},
	year = {2012},
	month = {2012},
	pages = {1113-1117},
	publisher = {IEEE},
	isbn = {1-4673-0382-8},
	author = {Fouzi Douak and Farid Melgani and Pasolli, Edoardo and Nabil Benoudjit}
}
@article {67291,
	title = {Color image compression algorithm based on the DCT transform combined to an adaptive block scanning},
	journal = {AEU-International Journal of Electronics and CommunicationsAEU-International Journal of Electronics and Communications},
	volume = {65},
	year = {2011},
	month = {2011},
	pages = {16-26},
	isbn = {1434-8411},
	author = {Fouzi Douak and Benzid, Redha and Nabil Benoudjit}
}
