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Leveraging Artificial Intelligence for Cyanobacterial Bloom Prediction: A Hybrid Deep Learning and Generative Adversarial Network Framework for Accurate Forecasting and Proactive Management Cover

Leveraging Artificial Intelligence for Cyanobacterial Bloom Prediction: A Hybrid Deep Learning and Generative Adversarial Network Framework for Accurate Forecasting and Proactive Management

Open Access
|Dec 2025

Authors

Nadjette Dendani

nadjet.dendani@univ-annaba.dz

LabGed Laboratory, Computer Science Department, Badji Mokhtar University, Annaba, Algeria

Amel Saoudi

amelsaoudi@yahoo.fr

Ecobiology Laboratory for Marine Environments and Coastal Areas,Biochemistry Department, Badji Mokhtar University, Annaba, Algeria

Nour Djihane Amara

nourjiheneamara@gmail.com

LabGed Laboratory, Computer Science Department, Badji Mokhtar University, Annaba, Algeria

Nabiha Azizi

nabiha111@yahoo.fr

LabGed Laboratory, Computer Science Department, Badji Mokhtar University, Annaba, Algeria

Julie Dugdale

julie.dugdale@univ-grenoble-alpes.fr

Grenoble Informatics Laboratory, Grenoble Alps University, France

Rayenne Hadiby

rayene.hadiby@univsite-paris-saclay.fr

Public Health, Paris Saclay University
DOI: https://doi.org/10.2478/fcds-2025-0017 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 427 - 450
Submitted on: Feb 25, 2025
Accepted on: Nov 5, 2025
Published on: Dec 8, 2025
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Nadjette Dendani, Amel Saoudi, Nour Djihane Amara, Nabiha Azizi, Julie Dugdale, Rayenne Hadiby, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.