Hasna Chamlal
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hasna Chamlal has a total of 31 co-authors. Here are the top co-authors by number of shared publications:
- Tayeb Ouaderhman
Computer Science and Systems Laboratory (LIS), Department of Mathematics and computer science, Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Casablanca, Morocco
12 shared publications Tayeb Ouaderhman
4 shared publications- Asmaa Benzmane2 shared publications
- Bazzi Mehdi2 shared publications
- Fatima Ezzahra Rebbah
Department of Mathematics and Informatics, Fundamental and Applied Mathematics Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco
2 shared publications - Fatima Zahra Janane
Department of Mathematics and Informatics, Fundamental and Applied Mathematics laboratory, Faculty of Sciences Ain chock, Hassan II University, Casablanca, Morroco
2 shared publications
Latest Publications
- 22 October 2026
- Published by Springer Nature in Cluster Computing
- Vol. 29 (15) , 881
FireFly inspired manipulator for imbalanced DDoS attack dataset
- 22 October 2026
- Published by Springer Nature in Cluster Computing
- Vol. 29 (15) , 881
FireFly inspired manipulator for imbalanced DDoS attack dataset
- 2 October 2026
- Published by Springer Nature
- p. 285-294
Multi-agent Q-Learning with Tensorized Q-Function for Adaptive Genetic Algorithms in High-Dimensional Classification
- 2 October 2026
- Published by Springer Nature
- p. 285-294
Multi-agent Q-Learning with Tensorized Q-Function for Adaptive Genetic Algorithms in High-Dimensional Classification
- 29 January 2026
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 17 (2) , 49
LI-HML detector: a unified framework for network traffic attack classification
- 29 January 2026
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 17 (2) , 49
LI-HML detector: a unified framework for network traffic attack classification
- 13 June 2025
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 158
A new filter based-instance selection for high dimensional data classification
- 13 June 2025
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 158
A new filter based-instance selection for high dimensional data classification
- 2 June 2025
- Published by Elsevier in Applied Soft Computing
- Vol. 180
An ensemble classifier combining Dempster–Shafer theory and feature selection methods aggregation strategy
- 2 June 2025
- Published by Elsevier in Applied Soft Computing
- Vol. 180
An ensemble classifier combining Dempster–Shafer theory and feature selection methods aggregation strategy
- Top Cited PapersOpen Access
- 18 March 2025
- Published by MDPI AG in Mathematics
- Vol. 13 (6) , 996
Mathematical Methods in Feature Selection: A Review
Feature selection is essential in machine learning and data science. Recently, there has been a growing effort to apply various mathematical methods to construct novel feature selection algorithms. In this study, we present a comprehensive state-of-the-art review of such techniques. We propose a new mathematical framework-based taxonomy to group the existing literature and provide an analysis of the research in each category from a mathematical perspective. The key frameworks discussed include variance-based methods, regularization methods, and Bayesian methods. By analyzing the strengths and limitations of each technique, we provide insights into their applicability across various domains. The review concludes with emerging trends and future research directions for mathematical methods in feature selection.
- 18 March 2025
- Published by MDPI AG in Mathematics
- Vol. 13 (6) , 996
Top Cited PapersOpen AccessMathematical Methods in Feature Selection: A Review
Feature selection is essential in machine learning and data science. Recently, there has been a growing effort to apply various mathematical methods to construct novel feature selection algorithms. In this study, we present a comprehensive state-of-the-art review of such techniques. We propose a new mathematical framework-based taxonomy to group the existing literature and provide an analysis of the research in each category from a mathematical perspective. The key frameworks discussed include variance-based methods, regularization methods, and Bayesian methods. By analyzing the strengths and limitations of each technique, we provide insights into their applicability across various domains. The review concludes with emerging trends and future research directions for mathematical methods in feature selection.
- Open Access
- 13 February 2025
- Published by MDPI AG in Diagnostics
- Vol. 15 (4) , 457
Clinical Impact of Neutrophil Variation on COVID-19 Complications
Background/Objectives: Corona virus disease 2019 (COVID-19) poses a threat to global public health. The early identification of critical cases is crucial to providing timely treatment to patients. Here, we investigated whether the neutrophil levels could predict COVID-19 complications. Methods: We performed a retrospective study of patients with COVID-19, admitted to the Cheikh Khalifa International University Hospital, Casablanca, Morocco. Laboratory test results collected upon admission and during hospitalization were analyzed based on clinical information. Results: Our study revealed that a rise in neutrophil “PNN” levels was associated with respiratory deterioration and intubation. They were positively correlated with the procalcitonin and C-reactive protein levels. Interestingly, PNN (polynuclear neutrophil) levels on day 5 proved to be a better predictor of intubation, acute respiratory distress syndrome (ARDS), and mortality than the initial PNN counts, C-reactive protein, or procalcitonin. Moreover, binary logistic regression with stratified PNN-day 5 data revealed that a PNN level on day 5 > 7.7 (109/L) was an independent risk factor for mortality and ARDS. Finally, the PNN levels on day 5 and proinflammatory cytokine IL-6 were positively correlated. Conclusions: Our data showed that neutrophilia proved to be an excellent predictor of complications and mortality during hospitalization and could be used to improve the management of patients with COVID-19.
- 13 February 2025
- Published by MDPI AG in Diagnostics
- Vol. 15 (4) , 457
Open AccessClinical Impact of Neutrophil Variation on COVID-19 Complications
Background/Objectives: Corona virus disease 2019 (COVID-19) poses a threat to global public health. The early identification of critical cases is crucial to providing timely treatment to patients. Here, we investigated whether the neutrophil levels could predict COVID-19 complications. Methods: We performed a retrospective study of patients with COVID-19, admitted to the Cheikh Khalifa International University Hospital, Casablanca, Morocco. Laboratory test results collected upon admission and during hospitalization were analyzed based on clinical information. Results: Our study revealed that a rise in neutrophil “PNN” levels was associated with respiratory deterioration and intubation. They were positively correlated with the procalcitonin and C-reactive protein levels. Interestingly, PNN (polynuclear neutrophil) levels on day 5 proved to be a better predictor of intubation, acute respiratory distress syndrome (ARDS), and mortality than the initial PNN counts, C-reactive protein, or procalcitonin. Moreover, binary logistic regression with stratified PNN-day 5 data revealed that a PNN level on day 5 > 7.7 (109/L) was an independent risk factor for mortality and ARDS. Finally, the PNN levels on day 5 and proinflammatory cytokine IL-6 were positively correlated. Conclusions: Our data showed that neutrophilia proved to be an excellent predictor of complications and mortality during hospitalization and could be used to improve the management of patients with COVID-19.
- 9 December 2024
- Published by Elsevier in Expert Systems with Applications
- Vol. 266
AL-FEW: An enhanced approach for optimized query examples through feature weighting in active learning
- 9 December 2024
- Published by Elsevier in Expert Systems with Applications
- Vol. 266
AL-FEW: An enhanced approach for optimized query examples through feature weighting in active learning
- 9 December 2024
- Published by Springer Nature in Knowledge and Information Systems
- Vol. 67 (1) , 633-660
A graph partitioning-based hybrid feature selection method in microarray datasets
- 9 December 2024
- Published by Springer Nature in Knowledge and Information Systems
- Vol. 67 (1) , 633-660
A graph partitioning-based hybrid feature selection method in microarray datasets
- 23 September 2024
- Published by Elsevier in Applied Soft Computing
- Vol. 167
A preordonance-based decision tree method and its parallel implementation in the framework of Map-Reduce
- 23 September 2024
- Published by Elsevier in Applied Soft Computing
- Vol. 167
A preordonance-based decision tree method and its parallel implementation in the framework of Map-Reduce