Publications internationales

2019
S Cheriguene, N Azizi, N Dey, A S. Ashour, A Ziani. (2019), A new hybrid classifier selection model based on mRMR method and diversity measures. International Journal of Machine Learning and Cybernetics 10 (5), 1189-1204 , 2019 , SPRINGER( Impact Factor 2.69) : SPRINGER, https://link.springer.com/article/10.1007/s13042-018-0797-6

Résumé: Classifier subset selection becomes an important stage in multiple classifier systems (MCSs) design to reduce the number of classifiers by eliminating the identical and inaccurate members. Minimum redundancy maximum relevance (mRMR) is a feature selection method that compromises between relevance and redundancy by obliterating similar members and keeping the most pertinent ones. In the current work, a novel classifier subset selection method based on mRMR method and diversity measures is proposed for building an efficient classifier ensemble. The proposed selection model suggested the greedy search algorithm using diversity-accuracy criteria to determine the optimal classifier set. The disagreement and Q-statistic measures are calculated to estimate the diversity among the members. Furthermore, the relevance is used as a means to determine the accuracy of the ensemble and its members. The experimental results over 24 datasets from the UCI repository and Kuncheva collection for real datasets are tested. The results established the efficiency of the proposed selection method with superior performance compared to the popular ensembles and several selection methods.

Chapitres de livres

2023
Samira Boughandjioua; Fares Laouacheria; Nabiha Azizi. (2023), Machine Learning Algorithms Investigation for Urban Drainage Decision Systems: Overview. 2023 International Conference on Decision Aid Sciences and Applications (DASA) : IEEE, https://ieeexplore.ieee.org/document/10286621

Résumé: Urban drainage systems may derive advantages from the implementation of machine learning methods for decision-making and cleansing operations. Conventional decision support systems are rendered ineffective in tackling the intricate and indeterminate aspects of urban planning concerns. This paper provides an overview of machine learning methods applied in modeling urban drainage systems, which are classified into five distinct approaches: Supervised learning, unsupervised learning, deep learning, Reinforcement learning, and finally hybrid approaches combining two or more previous algorithms. This study explores also diverse datasets that researchers can utilize in their scientific investigations. The present related works exploration reveals that a majority of studies lean towards reinforcement learning and deep learning methods, predominantly utilizing local datasets. Consequently, we have compiled a table containing some reference datasets. These advancements result in enhanced accuracy and efficiency in predicting and managing urban drainage systems.

Nacer Eddine Benzebouchi; Mohamed Ouassim Bourzama; Nawel Zemmal; Nabiha Azizi. (2023), Deep Classifier Fusion-based Multi-Classification of Chest X-rays for COVID-19 Detection. 2023 International Conference on Decision Aid Sciences and Applications (DASA) : IEEE, https://ieeexplore.ieee.org/document/10286599

Résumé: In the last three years, The COVID-19 pandemic has placed immense strain on healthcare systems worldwide. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing COVID-19. Recently, Computer Aided Diagnosis systems based on Deep Learning have been widely used to identify COVID-19 from other upper respiratory diseases such as viral pneumonia. To enhance COVID-19 diagnosis using CXR images, this article proposes a methodology that combines three deep learning models renowned for their depth and feature extraction capabilities: ResNet, DenseNet, and VGG. The model’ strengths are harnessed using Aggregation techniques such as stacking and voting (majority, weighted, average), to improve prediction accuracy. Moreover, the used dataset was subjected to preprocessing, balancing, and augmentation techniques, including rotation and zooming, to ensure robust recognition of CXR images, even in challenging positions. Experimental results demonstrate that the combination of model ensembles and enhanced dataset leads to superior accuracy in diagnosing COVID-19, with an impressive 97.95% accuracy, 98.2% F1 score, and 99.9% AUC-ROC. This approach not only outperforms individual models but also offers a more reliable and efficient solution for COVID-19 detection, particularly in complex cases where symptoms overlap with other respiratory illnesses.

2022
Boukhamla, A. , Bouziane, M.H. , Laib, A. , .Nabiha AZIZI, Merah, A. , Chaib, R.. (2022), GANs Investigation for Multimodal Medical Data Interpretation : Basic Architectures and Overview. 2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023, : IEEE, https://ieeexplore.ieee.org/document/10152386

Résumé: Medical imaging technologies have drastically changed the way healthcare professionals diagnose and treat patients. However, medical imaging datasets are limited in terms of size and diversity, which can lead to inaccurate diagnosis and treatment plans. To overcome these limitations, Generative Adversarial Networks (GANs) have been used to generate new medical images based on existing datasets. GANs are a type of deep learning artificial intelligence that can use existing datasets to create new synthetic images that look almost indistinguishable from real images. GANs can be used to generate medical images of organs, tissues, and diseases that are not present in existing datasets, providing healthcare professionals with more accurate and diverse datasets to diagnose and treat patients. GANs for medical image synthesis is a powerful tool that can revolutionize the healthcare industry. In this paper an investigation of the most used Gans architecture are presented. a state of the art of the latest works applying Gans are also discussed based on the adopted modality.

Rim Chaib; Nabiha Azizi; Nacer Eddine Hammami; Ibtissem Gasmi; Didier Schwab; Amira Chaib. ( 2022), GL-LSTM Model For Multi-label Text Classification Of Cardiovascular Disease Reports. 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) : IEEE, https://ieeexplore.ieee.org/document/9738147https://ieeexplore.ieee.org/document/9738147

Résumé: In recent years, the rapid growth of electronic data and information has gotten a lot of attention to find relevant knowledge such as textual information. The goal of automatic text classification is to automatically predict textual articles classes, especially in the medical domain. However, for some applications, the used data must inherently be described by more than one label. In this research, a new scheme of medical multi-label text classification is investigated which is based on intelligent engineering features using GloVe technique and LSTM classifier. The main particularity of GloVe permits the extraction of informative features to the word level automatically and capture the global and local textual semantics. The choice of the LSTM model is motivated by the success that has been achieved by taking into account the very long-term dependencies between words. The experiment of our approach named GL-LSTM based on Ohsumed cardiovascular text dataset has produced impressive results with an overall accuracy of 0.927 compared with related works existing in the literature

2022
Boukhamla, A. , Bouziane, M.H. , Laib, A. , .Nabiha AZIZI, Merah, A. , Chaib, R.. (2022), GANs Investigation for Multimodal Medical Data Interpretation : Basic Architectures and Overview. 2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023, : IEEE, https://ieeexplore.ieee.org/document/10152386

Résumé: Medical imaging technologies have drastically changed the way healthcare professionals diagnose and treat patients. However, medical imaging datasets are limited in terms of size and diversity, which can lead to inaccurate diagnosis and treatment plans. To overcome these limitations, Generative Adversarial Networks (GANs) have been used to generate new medical images based on existing datasets. GANs are a type of deep learning artificial intelligence that can use existing datasets to create new synthetic images that look almost indistinguishable from real images. GANs can be used to generate medical images of organs, tissues, and diseases that are not present in existing datasets, providing healthcare professionals with more accurate and diverse datasets to diagnose and treat patients. GANs for medical image synthesis is a powerful tool that can revolutionize the healthcare industry. In this paper an investigation of the most used Gans architecture are presented. a state of the art of the latest works applying Gans are also discussed based on the adopted modality.

Rim Chaib; Nabiha Azizi; Nacer Eddine Hammami; Ibtissem Gasmi; Didier Schwab; Amira Chaib. ( 2022), GL-LSTM Model For Multi-label Text Classification Of Cardiovascular Disease Reports. 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) : IEEE, https://ieeexplore.ieee.org/document/9738147https://ieeexplore.ieee.org/document/9738147

Résumé: In recent years, the rapid growth of electronic data and information has gotten a lot of attention to find relevant knowledge such as textual information. The goal of automatic text classification is to automatically predict textual articles classes, especially in the medical domain. However, for some applications, the used data must inherently be described by more than one label. In this research, a new scheme of medical multi-label text classification is investigated which is based on intelligent engineering features using GloVe technique and LSTM classifier. The main particularity of GloVe permits the extraction of informative features to the word level automatically and capture the global and local textual semantics. The choice of the LSTM model is motivated by the success that has been achieved by taking into account the very long-term dependencies between words. The experiment of our approach named GL-LSTM based on Ohsumed cardiovascular text dataset has produced impressive results with an overall accuracy of 0.927 compared with related works existing in the literature