@article {Benlakhdar20221599,
	title = {Statistical modeling of directional data using a robust hierarchical von mises distribution model: perspectives for wind energy},
	journal = {Computational Statistics},
	volume = {37},
	number = {4},
	year = {2022},
	note = {cited By 0},
	pages = {1599-1619},
	abstract = {For describing wind direction, a variety of statistical distributions has been suggested that provides information about the wind regime at a particular location and aids the development of efficient wind energy generation. In this paper a systematic approach for data classification putting a special emphasis on the von Mises mixtures is presented. A von Mises mixture model is broad enough to cover, on one hand, symmetry and asymmetry, on the other hand, unimodality and multimodality of circular data. We developed an improved mathematical model of the classical von Mises mixture method, rests on number of principles which gives its internal coherence and originality. In principle, our hierarchical model of von Mises distributions is flexible to precisely modeled complex directional data sets. We define a new specific expectation{\textendash}maximization (S-EM) algorithm for estimating the parameters of the model. The simulation showed that satisfactory fit of complex directional data could be obtained (error generally < 1\%). Furthermore, the Bayesian Information Criterion is used to judge the goodness of fit and the suitability for this model versus common distributions found in the literature. The findings prove that our hierarchical model of von Mises distributions is relevant for modeling the complex directional data with several modes and/or prevailing data directions. {\textcopyright} 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},
	doi = {10.1007/s00180-021-01173-5},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122431362\&doi=10.1007\%2fs00180-021-01173-5\&partnerID=40\&md5=1983e074fcfeb1ae4f99796700245614},
	author = {Benlakhdar, S. and Rziza, M. and Thami, R.O.H.}
}
@article {Benlakhdar20201659,
	title = {A robust model using SIFT and gamma mixture model for texture images classification: Perspectives for medical applications},
	journal = {Biomedical and Pharmacology Journal},
	volume = {13},
	number = {4},
	year = {2020},
	note = {cited By 0},
	pages = {1659-1669},
	abstract = {The texture analysis of medical images is a powerful calculation tool for the discrimination between pathological and healthy tissue in different organs in medical images. Our paper proposes a novel approach named, GGD-GMM, based on statistical modeling in wavelet domain to describe texture images. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform (SIFT). Secondly, we implement the aforementioned algorithm and fit the result by using the finite Gamma Mixture Model (GMM). The results, obtained for two benchmark datasets, show that our proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models. Moreover, it displays others advantages relied to Noise-resistant and rotation invariant. Our algorithm could be useful for the analysis of several medical issues. {\textcopyright} 2020 This is an Open Access article licensed under a Creative Commons license: Attribution 4.0 International (CC-BY). Published by Oriental Scientific Publishing Company},
	keywords = {Article, curvelet transform, human, human experiment, noise, rotation, scale invariant feature transform},
	doi = {10.13005/BPJ/2041},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100517546\&doi=10.13005\%2fBPJ\%2f2041\&partnerID=40\&md5=8997e98fae845d62807a15ca1c749766},
	author = {Benlakhdar, S. and Rziza, M. and Thami, R.O.H.}
}
@conference {Benlakhdar2017,
	title = {Directional data classification using a hierarchical model of von Mises distribution},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {The von Mises distribution1 VM -pdf is a continuous probability distribution on the circle used in directional statistics. A mixture model of von Mises distribution, which is broad enough to cover symmetry as well as asymmetry, unimodality as well as multimodality of circular data. In this paper we use a model comprised of a hierarchical von Mises mixture distribution mode HmvM- pdf where we consider each class is itself the result of a mixture of subclasses. The parameters of our model are estimated using the expectation maximization algorithm EM modified. The HmvM- pdf model achieves higher accuracy than the mvM model and offer a rich modeling. The suitability of the distributions is judged from the coefficient of determination R2. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090425},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028459799\&doi=10.1145\%2f3090354.3090425\&partnerID=40\&md5=ce3dab33a54f11b997bc9f8f24999c78},
	author = {Benlakhdar, S. and Rziza, M. and Thami, R.O.H.}
}
