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Showing 1 to 12 of 33 entries
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Variational learning for finite Dirichlet mixture models and applications.

IEEE transactions on neural networks and learning systems

Fan W, Bouguila N, Ziou D.
PMID: 24806125
IEEE Trans Neural Netw Learn Syst. 2012 May;23(5):762-74. doi: 10.1109/TNNLS.2012.2190298.

In this paper, we focus on the variational learning of finite Dirichlet mixture models. Compared to other algorithms that are commonly used for mixture models (such as expectation-maximization), our approach has several advantages: first, the problem of over-fitting is...

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Fan W, Bouguila N, Ziou D. Variational learning for finite Dirichlet mixture models and applications. IEEE Trans Neural Netw Learn Syst. 2012;23(5):762-74doi: 10.1109/TNNLS.2012.2190298.
Fan, W., Bouguila, N., & Ziou, D. (2012). Variational learning for finite Dirichlet mixture models and applications. IEEE transactions on neural networks and learning systems, 23(5), 762-74. https://doi.org/10.1109/TNNLS.2012.2190298
Fan, Wentao, et al. "Variational learning for finite Dirichlet mixture models and applications." IEEE transactions on neural networks and learning systems vol. 23,5 (2012): 762-74. doi: https://doi.org/10.1109/TNNLS.2012.2190298
Fan W, Bouguila N, Ziou D. Variational learning for finite Dirichlet mixture models and applications. IEEE Trans Neural Netw Learn Syst. 2012 May;23(5):762-74. doi: 10.1109/TNNLS.2012.2190298. PMID: 24806125.
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A hybrid feature extraction selection approach for high-dimensional non-Gaussian data clustering.

IEEE transactions on pattern analysis and machine intelligence

Boutemedjet S, Bouguila N, Ziou D.
PMID: 19542577
IEEE Trans Pattern Anal Mach Intell. 2009 Aug;31(8):1429-43. doi: 10.1109/TPAMI.2008.155.

This paper presents an unsupervised approach for feature selection and extraction in mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture model that is able to extract independent and non-Gaussian features without loss of accuracy. The...

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Boutemedjet S, Bouguila N, Ziou D. A hybrid feature extraction selection approach for high-dimensional non-Gaussian data clustering. IEEE Trans Pattern Anal Mach Intell. 2009;31(8):1429-43doi: 10.1109/TPAMI.2008.155.
Boutemedjet, S., Bouguila, N., & Ziou, D. (2009). A hybrid feature extraction selection approach for high-dimensional non-Gaussian data clustering. IEEE transactions on pattern analysis and machine intelligence, 31(8), 1429-43. https://doi.org/10.1109/TPAMI.2008.155
Boutemedjet, Sabri, et al. "A hybrid feature extraction selection approach for high-dimensional non-Gaussian data clustering." IEEE transactions on pattern analysis and machine intelligence vol. 31,8 (2009): 1429-43. doi: https://doi.org/10.1109/TPAMI.2008.155
Boutemedjet S, Bouguila N, Ziou D. A hybrid feature extraction selection approach for high-dimensional non-Gaussian data clustering. IEEE Trans Pattern Anal Mach Intell. 2009 Aug;31(8):1429-43. doi: 10.1109/TPAMI.2008.155. PMID: 19542577.
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B-cell lymphoma of the mandible: a case report.

Clinical medicine. Oncology

Adouani A, Bouguila J, Jeblaoui Y, Ben Aicha M, Abdelali MA, Hellali M, Zitouni K, Amani L, Issam Z.
PMID: 21892315
Clin Med Oncol. 2008;2:445-50. doi: 10.4137/cmo.s366. Epub 2008 Jun 11.

INTRODUCTION: The mandible is an infrequent localisation of primary osseous non-Hodgkin's lymphomas. Few cases of mandibular non-Hodgkin's lymphomas (NHL) have been reported.CASE REPORT: A rare condition of primary malignant non-Hodgkin's lymphoma of the mandible in 53-year-old man, was reported...

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Adouani A, Bouguila J, Jeblaoui Y, et al. B-cell lymphoma of the mandible: a case report. Clin Med Oncol. 2008;2:445-50doi: 10.4137/cmo.s366.
Adouani, A., Bouguila, J., Jeblaoui, Y., Ben Aicha, M., Abdelali, M. A., Hellali, M., Zitouni, K., Amani, L., & Issam, Z. (2008). B-cell lymphoma of the mandible: a case report. Clinical medicine. Oncology, 2445-50. https://doi.org/10.4137/cmo.s366
Adouani, Ali, et al. "B-cell lymphoma of the mandible: a case report." Clinical medicine. Oncology vol. 2 (2008): 445-50. doi: https://doi.org/10.4137/cmo.s366
Adouani A, Bouguila J, Jeblaoui Y, Ben Aicha M, Abdelali MA, Hellali M, Zitouni K, Amani L, Issam Z. B-cell lymphoma of the mandible: a case report. Clin Med Oncol. 2008;2:445-50. doi: 10.4137/cmo.s366. Epub 2008 Jun 11. PMID: 21892315; PMCID: PMC3161690.
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Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images.

Journal of imaging

Bourouis S, Alharbi A, Bouguila N.
PMID: 34460578
J Imaging. 2021 Jan 10;7(1). doi: 10.3390/jimaging7010007.

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people....

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Bourouis S, Alharbi A, Bouguila N. Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images. J Imaging. 2021;7(1)doi: 10.3390/jimaging7010007.
Bourouis, S., Alharbi, A., & Bouguila, N. (2021). Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images. Journal of imaging, 7(1), . https://doi.org/10.3390/jimaging7010007
Bourouis, Sami, et al. "Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images." Journal of imaging vol. 7,1 (2021). doi: https://doi.org/10.3390/jimaging7010007
Bourouis S, Alharbi A, Bouguila N. Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images. J Imaging. 2021 Jan 10;7(1). doi: 10.3390/jimaging7010007. PMID: 34460578; PMCID: PMC8321244.
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Clustering Analysis via Deep Generative Models With Mixture Models.

IEEE transactions on neural networks and learning systems

Yang L, Fan W, Bouguila N.
PMID: 33048769
IEEE Trans Neural Netw Learn Syst. 2022 Jan;33(1):340-350. doi: 10.1109/TNNLS.2020.3027761. Epub 2022 Jan 05.

Clustering is a fundamental problem that frequently arises in many fields, such as pattern recognition, data mining, and machine learning. Although various clustering algorithms have been developed in the past, traditional clustering algorithms with shallow structures cannot excavate the...

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Yang L, Fan W, Bouguila N. Clustering Analysis via Deep Generative Models With Mixture Models. IEEE Trans Neural Netw Learn Syst. 2022;33(1):340-350doi: 10.1109/TNNLS.2020.3027761.
Yang, L., Fan, W., & Bouguila, N. (2022). Clustering Analysis via Deep Generative Models With Mixture Models. IEEE transactions on neural networks and learning systems, 33(1), 340-350. https://doi.org/10.1109/TNNLS.2020.3027761
Yang, Lin, et al. "Clustering Analysis via Deep Generative Models With Mixture Models." IEEE transactions on neural networks and learning systems vol. 33,1 (2022): 340-350. doi: https://doi.org/10.1109/TNNLS.2020.3027761
Yang L, Fan W, Bouguila N. Clustering Analysis via Deep Generative Models With Mixture Models. IEEE Trans Neural Netw Learn Syst. 2022 Jan;33(1):340-350. doi: 10.1109/TNNLS.2020.3027761. Epub 2022 Jan 05. PMID: 33048769.
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Sparse Count Data Clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions.

IEEE transactions on neural networks and learning systems

Zamzami N, Bouguila N.
PMID: 33079676
IEEE Trans Neural Netw Learn Syst. 2022 Jan;33(1):89-102. doi: 10.1109/TNNLS.2020.3027539. Epub 2022 Jan 05.

Clustering frequency vectors is a challenging task on large data sets considering its high dimensionality and sparsity nature. Generalized Dirichlet multinomial (GDM) distribution is a competitive generative model for count data in terms of accuracy, yet its parameters estimation...

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Zamzami N, Bouguila N. Sparse Count Data Clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions. IEEE Trans Neural Netw Learn Syst. 2022;33(1):89-102doi: 10.1109/TNNLS.2020.3027539.
Zamzami, N., & Bouguila, N. (2022). Sparse Count Data Clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions. IEEE transactions on neural networks and learning systems, 33(1), 89-102. https://doi.org/10.1109/TNNLS.2020.3027539
Zamzami, Nuha, and Bouguila, Nizar. "Sparse Count Data Clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions." IEEE transactions on neural networks and learning systems vol. 33,1 (2022): 89-102. doi: https://doi.org/10.1109/TNNLS.2020.3027539
Zamzami N, Bouguila N. Sparse Count Data Clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions. IEEE Trans Neural Netw Learn Syst. 2022 Jan;33(1):89-102. doi: 10.1109/TNNLS.2020.3027539. Epub 2022 Jan 05. PMID: 33079676.
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Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures.

Sensors (Basel, Switzerland)

Bourouis S, Pawar Y, Bouguila N.
PMID: 35009726
Sensors (Basel). 2021 Dec 28;22(1). doi: 10.3390/s22010186.

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture...

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Bourouis S, Pawar Y, Bouguila N. Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures. Sensors (Basel). 2021;22(1)doi: 10.3390/s22010186.
Bourouis, S., Pawar, Y., & Bouguila, N. (2021). Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures. Sensors (Basel, Switzerland), 22(1), . https://doi.org/10.3390/s22010186
Bourouis, Sami, et al. "Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures." Sensors (Basel, Switzerland) vol. 22,1 (2021). doi: https://doi.org/10.3390/s22010186
Bourouis S, Pawar Y, Bouguila N. Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures. Sensors (Basel). 2021 Dec 28;22(1). doi: 10.3390/s22010186. PMID: 35009726.
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Brucellosis manifesting as reversible ataxia caused by SIADH-related hyponatremia.

IDCases

Habib MB, Hamid O, Bougaila A, Habib H, Mohamed MFH.
PMID: 33489758
IDCases. 2021 Jan 02;23:e01043. doi: 10.1016/j.idcr.2020.e01043. eCollection 2021.

Brucellosis is an infectious disease commonly presents with fever, fatigue, arthralgia, and muscle pain. Nonetheless, hyponatremia and its related symptoms have been reported as presenting features of brucellosis. Herein, we report a 56-year-old male presenting with ataxia and SIADH-related...

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Habib MB, Hamid O, Bougaila A, et al. Brucellosis manifesting as reversible ataxia caused by SIADH-related hyponatremia. IDCases. 2021;23:e01043doi: 10.1016/j.idcr.2020.e01043.
Habib, M. B., Hamid, O., Bougaila, A., Habib, H., & Mohamed, M. F. H. (2021). Brucellosis manifesting as reversible ataxia caused by SIADH-related hyponatremia. IDCases, 23e01043. https://doi.org/10.1016/j.idcr.2020.e01043
Habib, Mhd Baraa, et al. "Brucellosis manifesting as reversible ataxia caused by SIADH-related hyponatremia." IDCases vol. 23 (2021): e01043. doi: https://doi.org/10.1016/j.idcr.2020.e01043
Habib MB, Hamid O, Bougaila A, Habib H, Mohamed MFH. Brucellosis manifesting as reversible ataxia caused by SIADH-related hyponatremia. IDCases. 2021 Jan 02;23:e01043. doi: 10.1016/j.idcr.2020.e01043. eCollection 2021. PMID: 33489758; PMCID: PMC7804976.
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Maximum a Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection.

IEEE transactions on neural networks and learning systems

Ali S, Bouguila N.
PMID: 33929966
IEEE Trans Neural Netw Learn Syst. 2021 Apr 30; doi: 10.1109/TNNLS.2021.3071083. Epub 2021 Apr 30.

One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including...

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Ali S, Bouguila N. Maximum a Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection. IEEE Trans Neural Netw Learn Syst. 2021;doi: 10.1109/TNNLS.2021.3071083.
Ali, S., & Bouguila, N. (2021). Maximum a Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection. IEEE transactions on neural networks and learning systems, . https://doi.org/10.1109/TNNLS.2021.3071083
Ali, Samr, and Bouguila, Nizar. "Maximum a Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection." IEEE transactions on neural networks and learning systems vol. (2021). doi: https://doi.org/10.1109/TNNLS.2021.3071083
Ali S, Bouguila N. Maximum a Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection. IEEE Trans Neural Netw Learn Syst. 2021 Apr 30; doi: 10.1109/TNNLS.2021.3071083. Epub 2021 Apr 30. PMID: 33929966.
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Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings.

IEEE transactions on neural networks and learning systems

Yang L, Fan W, Bouguila N.
PMID: 34941534
IEEE Trans Neural Netw Learn Syst. 2021 Dec 23; doi: 10.1109/TNNLS.2021.3135460. Epub 2021 Dec 23.

In recent years, clustering methods based on deep generative models have received great attention in various unsupervised applications, due to their capabilities for learning promising latent embeddings from original data. This article proposes a novel clustering method based on...

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Yang L, Fan W, Bouguila N. Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings. IEEE Trans Neural Netw Learn Syst. 2021;doi: 10.1109/TNNLS.2021.3135460.
Yang, L., Fan, W., & Bouguila, N. (2021). Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings. IEEE transactions on neural networks and learning systems, . https://doi.org/10.1109/TNNLS.2021.3135460
Yang, Lin, et al. "Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings." IEEE transactions on neural networks and learning systems vol. (2021). doi: https://doi.org/10.1109/TNNLS.2021.3135460
Yang L, Fan W, Bouguila N. Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings. IEEE Trans Neural Netw Learn Syst. 2021 Dec 23; doi: 10.1109/TNNLS.2021.3135460. Epub 2021 Dec 23. PMID: 34941534.
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Quality of Life Determinants in Children and Adolescents with Mild to Moderate Asthma in Tunisia.

Comprehensive child and adolescent nursing

Dardouri M, Sahli J, Ajmi T, Mtiraoui A, Bouguila J, Mallouli M.
PMID: 32687718
Compr Child Adolesc Nurs. 2020 Jul 20;1-11. doi: 10.1080/24694193.2020.1789240. Epub 2020 Jul 20.

Chronic childhood asthma is a leading cause of poor quality of life. Factors associated with this major asthma outcome were controversial. The aim of this study is to assess the quality of life of children and adolescents with mild...

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Dardouri M, Sahli J, Ajmi T, et al. Quality of Life Determinants in Children and Adolescents with Mild to Moderate Asthma in Tunisia. Compr Child Adolesc Nurs. 2020;1-11doi: 10.1080/24694193.2020.1789240.
Dardouri, M., Sahli, J., Ajmi, T., Mtiraoui, A., Bouguila, J., & Mallouli, M. (2020). Quality of Life Determinants in Children and Adolescents with Mild to Moderate Asthma in Tunisia. Comprehensive child and adolescent nursing, 1-11. https://doi.org/10.1080/24694193.2020.1789240
Dardouri, Maha, et al. "Quality of Life Determinants in Children and Adolescents with Mild to Moderate Asthma in Tunisia." Comprehensive child and adolescent nursing vol. (2020): 1-11. doi: https://doi.org/10.1080/24694193.2020.1789240
Dardouri M, Sahli J, Ajmi T, Mtiraoui A, Bouguila J, Mallouli M. Quality of Life Determinants in Children and Adolescents with Mild to Moderate Asthma in Tunisia. Compr Child Adolesc Nurs. 2020 Jul 20;1-11. doi: 10.1080/24694193.2020.1789240. Epub 2020 Jul 20. PMID: 32687718.
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Online Learning of Hierarchical Pitman-Yor Process Mixture of Generalized Dirichlet Distributions With Feature Selection.

IEEE transactions on neural networks and learning systems

Fan W, Sallay H, Bouguila N.
PMID: 27305687
IEEE Trans Neural Netw Learn Syst. 2017 Sep;28(9):2048-2061. doi: 10.1109/TNNLS.2016.2574500. Epub 2016 Jun 09.

In this paper, a novel statistical generative model based on hierarchical Pitman-Yor process and generalized Dirichlet distributions (GDs) is presented. The proposed model allows us to perform joint clustering and feature selection thanks to the interesting properties of the...

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Fan W, Sallay H, Bouguila N. Online Learning of Hierarchical Pitman-Yor Process Mixture of Generalized Dirichlet Distributions With Feature Selection. IEEE Trans Neural Netw Learn Syst. 2016;28(9):2048-2061doi: 10.1109/TNNLS.2016.2574500.
Fan, W., Sallay, H., & Bouguila, N. (2017). Online Learning of Hierarchical Pitman-Yor Process Mixture of Generalized Dirichlet Distributions With Feature Selection. IEEE transactions on neural networks and learning systems, 28(9), 2048-2061. https://doi.org/10.1109/TNNLS.2016.2574500
Fan, Wentao, et al. "Online Learning of Hierarchical Pitman-Yor Process Mixture of Generalized Dirichlet Distributions With Feature Selection." IEEE transactions on neural networks and learning systems vol. 28,9 (2017): 2048-2061. doi: https://doi.org/10.1109/TNNLS.2016.2574500
Fan W, Sallay H, Bouguila N. Online Learning of Hierarchical Pitman-Yor Process Mixture of Generalized Dirichlet Distributions With Feature Selection. IEEE Trans Neural Netw Learn Syst. 2017 Sep;28(9):2048-2061. doi: 10.1109/TNNLS.2016.2574500. Epub 2016 Jun 09. PMID: 27305687.
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Showing 1 to 12 of 33 entries