Weighted ensemble learning of Bayesian network for gene regulatory networks H Njah, S Jamoussi Neurocomputing 150, 404-416, 2015 | 39 | 2015 |
Using general linear model, Bayesian Networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms W Feki-Sahnoun, H Njah, A Hamza, N Barraj, M Mahfoudi, A Rebai, ... Ecological Informatics 43, 12-23, 2018 | 37 | 2018 |
A Bayesian network approach to determine environmental factors controlling Karenia selliformis occurrences and blooms in the Gulf of Gabès, Tunisia W Feki-Sahnoun, A Hamza, H Njah, N Barraj, M Mahfoudi, A Rebai, ... Harmful algae 63, 119-132, 2017 | 35 | 2017 |
Deep Bayesian network architecture for Big Data mining H Njah, S Jamoussi, W Mahdi Concurrency and computation: practice and experience 31 (2), e4418, 2019 | 29 | 2019 |
Merits of Bayesian networks in overcoming small data challenges: A meta-model for handling missing data H Ameur, H Njah, S Jamoussi International Journal of Machine Learning and Cybernetics 14 (1), 229-251, 2023 | 10 | 2023 |
Using a naive Bayes classifier to explore the factors driving the harmful dinoflagellate Karenia selliformis blooms in a southeastern Mediterranean lagoon W Feki-Sahnoun, H Njah, A Hamza, N Barraj, M Mahfoudi, A Rebai, ... Ocean Dynamics 70, 897-911, 2020 | 9 | 2020 |
Influence of phosphorus-contaminated sediments in the abundance of potentially toxic phytoplankton along the Sfax Coasts (Gulf of Gabes, Tunisia) W Feki-Sahnoun, H Njah, N Barraj, M Mahfoudi, F Akrout, A Rebai, ... J. Sediment. Environ 4, 458-470, 2019 | 8 | 2019 |
A Bayesian approach to construct Context-Specific Gene Ontology: Application to protein function prediction H Njah, S Jamoussi, W Mahdi, M Elati 2016 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2016 | 5 | 2016 |
Semi-hierarchical naïve Bayes classifier H Njah, S Jamoussi, W Mahdi 2016 International Joint Conference on Neural Networks (IJCNN), 1772-1779, 2016 | 5 | 2016 |
A new equilibrium criterion for learning the cardinality of latent variables H Njah, S Jamoussi, W Mahdi, A Masmoudi 2015 IEEE 27th International Conference on Tools with Artificial …, 2015 | 5 | 2015 |
Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering H Njah, S Jamoussi, W Mahdi Annals of Mathematics and Artificial Intelligence 89 (10), 1013-1033, 2021 | 4 | 2021 |
A Naïve Bayesian network approach to determine the potential drivers of the toxic dinoflagellate Coolia monotis (Meunier, 1919) in the Gulf of Gabès, Tunisia W Feki-Sahnoun, H Njah, M Abdennadher, A Hamza, N Barraj, ... Euro-Mediterranean Journal for Environmental Integration 4, 1-11, 2019 | 3 | 2019 |
AZ-skin: Inclusive system for skin disease recognition from hybrid data A Zhiou, H Njah Multimedia Tools and Applications 83 (14), 43199-43221, 2024 | 2 | 2024 |
Interpretable Bayesian network abstraction for dimension reduction H Njah, S Jamoussi, W Mahdi Neural Computing and Applications 35 (14), 10031-10049, 2023 | 2 | 2023 |
Weighted committee-based structure learning for microarray data H Njah, S Jamoussi 13th IEEE International Conference on BioInformatics and BioEngineering, 1-4, 2013 | 1 | 2013 |
Multi-method Analysis for Early Diagnosis of Alzheimer's Disease on Magnetic Resonance Imaging (MRI) Using Deep Learning and Hybrid Methods D Guesmi, H Njah, YB Ayed International Conference on Computational Collective Intelligence, 470-487, 2024 | | 2024 |
A Naïve Bayesian Network Approach to Determine the Potential Drivers of the Toxic Dinoflagellate Coolia monotis in the Gulf of Gabès, Tunisia W Feki-Sahnoun, H Njah, M Abdennadher, A Hamza, N Barraj, ... Recent Advances in Environmental Science from the Euro-Mediterranean and …, 2018 | | 2018 |
INFLUENCE OF PHOSPHORUS-CONTAMINATED SEDIMENTS IN THE ABUNDANCE OF POTENTIALLY TOXIC PHYTOPLANKTON ALONG THE SFAX COASTS W FEKI-SAHNOUN, H NJAH, N BARRAJ, M MAHFOUDI, F AKROUT, ... | | |
A Study of Key Genes’ Behavior for Amyotrophic Lateral Sclerosis Disease H Njah, MC Karray, S Jamoussi, YB Ayed International Conference for Engineering Sciences for Biology and Medicine, 83, 0 | | |