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Imad RIDA

Associate Professor

Université de Technologie de Compiègne

France

Research Interests

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About

Imad RIDA is an Associate Professor at Université de Technologie de Compiègne in France. His research focuses on deep learning techniques applied to various fields, including healthcare, smart surveillance, and biometrics. He has contributed to recent publications on topics such as human-inspired saliency detection, sEMG signal classification, and activity recognition in the Internet of Medical Things.

Articles (16)

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2021

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2021

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2021

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2020

Collaborators (4)

Muhammad Waqas

University of Greenwich

UNITED KINGDOM

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Muhammad Talha Usman

Graduate Researcher

Gachon University

SOUTH KOREA

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JaKeoung Koo

Assistant Professor

Gachon University

SOUTH KOREA

View details

sofiane boudaoud

Professeur des universités

Université de Technologie de Compiègne

FRANCE

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Imad RIDA

Research Interests

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About

Articles (16)

An efficient deep learning architecture for effective fire detection in smart surveillance

Authenticating and securing healthcare records: A deep learning-based zero watermarking approach

Deep Learning Approach for Hand Gesture Recognition: Applications in Deaf Communication and Healthcare

Dense Hybrid Attention Network for Palmprint Image Super-Resolution

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Graph Autoencoder Anomaly Detection for E-Commerce Application by Contextual Integrating Contrast With Reconstruction and Complementarity

Human centric attention with deep multiscale feature fusion framework for activity recognition in Internet of Medical Things

Journey into gait biometrics: Integrating deep learning for enhanced pattern recognition

Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation

MIWET: Medical image watermarking using encryption and fusion technique

Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches

Attention enhanced machine instinctive vision with human-inspired saliency detection

Towards an efficient real-time kernel function stream clustering method via shared nearest-neighbor density for the IIoT

A Multifeature Learning and Fusion Network for Facial Age Estimation

Jointly Heterogeneous Palmprint Discriminant Feature Learning.

A comprehensive overview of feature representation for biometric recognition

Collaborators (4)

Muhammad Waqas

Muhammad Talha Usman

JaKeoung Koo

sofiane boudaoud

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