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2020 – today
- 2025
[j6]Aya Ferchichi, Ahlem Ferchichi, Fatma Hendaoui
, Mejda Chihaoui, Radhia Toujani:
Deep Learning-based Uncertainty Quantification for spatio-temporal environmental Remote Sensing: A systematic literature review. Neurocomputing 639: 130242 (2025)- [j5]Radhia Toujani, Fatma Hendaoui, Ahlem Ferchichi, Mejda Chihaoui, Aya Ferchichi, Wided Bakari:
Generating prediction clustering graph network analysis data from masked time series with GANs. Neural Comput. Appl. 37(14): 8699-8729 (2025) - [j4]Ahlem Ferchichi, Mejda Chihaoui, Radhia Toujani, Aya Ferchichi, Fatma Hendaoui:
Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting. Neural Comput. Appl. 37(15): 8773-8797 (2025) - [c1]Aya Ferchichi, Ali Ben Abbes, Vincent Barra, Imed Riadh Farah:
Trustworthy AI for Spatio-Temporal Forecasting via Counterfactual Causality. ICTAI 2025: 10-17 - 2024
- [j3]Aya Ferchichi, Ali Ben Abbes, Vincent Barra, Manel Rhif, Imed Riadh Farah:
Multi-attention Generative Adversarial Network for multi-step vegetation indices forecasting using multivariate time series. Eng. Appl. Artif. Intell. 128: 107563 (2024) - [j2]Ahlem Ferchichi, Mejda Chihaoui, Aya Ferchichi:
Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa. Expert Syst. Appl. 238(Part F): 122211 (2024) - 2022
- [j1]Aya Ferchichi, Ali Ben Abbes, Vincent Barra, Imed Riadh Farah:
Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review. Ecol. Informatics 68: 101552 (2022)
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