Adel Hafiane

Associate Professor

France

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About

Adel Hafiane is an Associate Professor at INSA CVL in France. His research primarily focuses on decentralized federated learning, multimodal datasets, and image-based analysis, particularly in the context of agriculture and disaster response. Recent publications include topics such as crop disease classification, crisis tweet categorization, and advanced methodologies for image detection and data labeling.

Articles (16)

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2025

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2021

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2021

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2020

Collaborators (4)

Nicolas RAGOT

Professor

Université de Tours Laboratoire d'Informatique Fondamentale et Appliquée de Tours

FRANCE

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Laurent Bobelin

Assistant Professor

Institut National des Sciences Appliquées Centre Val de Loire

FRANCE

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Karim Abed-Meraim

FRANCE

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Raphaël Canals

University of Orléans

FRANCE

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Adel Hafiane

Research Interests

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Articles (16)

Decentralized federated learning using validation loss for model sharing in crop disease classification

OPIT: A Simple but Effective Method for Sparse Subspace Tracking in High-Dimension and Low-Sample-Size Context

ConvNeXt based semi-supervised approach with consistency regularization for weeds classification

Image-text crisis tweet categorization:a caption-based approach

M-CATNAT: A Multimodal dataset to analyze French tweets during natural disasters

Hierarchical graph representation for unsupervised crop row detection in images

Fusion of Satellite Images and Weather Data With Transformer Networks for Downy Mildew Disease Detection

Image-based crop disease detection with federated learning

Generative-Model-Based Data Labeling for Deep Network Regression: Application to Seed Maturity Estimation from UAV Multispectral Images

Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images

A Contemporary and Comprehensive Survey on Streaming Tensor Decomposition

Robust Tensor Tracking With Missing Data and Outliers: Novel Adaptive CP Decomposition and Convergence Analysis

Shape constraint function for artery tracking in ultrasound images

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

A deep learning based multiscale approach to segment the areas of interest in whole slide images

CRowNet: Deep Network for Crop Row Detection in UAV Images

Collaborators (4)

Nicolas RAGOT

Laurent Bobelin

Karim Abed-Meraim

Raphaël Canals

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