Veg-W2TCN: A parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model

M Rhif, AB Abbes, B Martínez, IR Farah - Applied Soft Computing, 2023 - Elsevier
Long-term vegetation time series (TS) forecasting based on climatic data is one of the most
challenging topics, capable of assisting in advanced estimation and management for …

Intelligent fault diagnosis of rolling bearingbased on deep transfer learning using time-frequency representation

M Kavianpour, M Ghorvei, A Ramezani… - 2021 7th …, 2021 - ieeexplore.ieee.org
With the expansion of deep learning (DL) and machine learning (ML) methods, fault
diagnosis based on data-driven models has recently become controversial. However, due to …

Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach

M Kakooei, K Solska, A Daoud - arxiv preprint arxiv:2411.02855, 2024 - arxiv.org
Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs).
Achieving this requires high-frequency, granular data to capture neighborhood-level …

Desertification detection based on landsat time-series images and variational auto-encoder: application in Jeffera, Tunisia

C Farah, R Manel, AB Abbes… - IGARSS 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Desertification detection is a challenging problem because of the dynamic climate change
and human activity. This paper proposes a methodology to detect regions with …

Data-Driven Forecasting of Climate Change Impacts on Vegetation for Sustainable Agriculture

M Rhif, AB Abbes, B Martínez, IR Farah - Procedia Computer Science, 2024 - Elsevier
Accurate forecasting of climate and vegetation changes through SDG 13 initiatives is critical
for adapting agricultural practices, as it allows farmers to anticipate changes in growing …