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H. Mathkour

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Open access Aug 2026

Hybrid Landmark-Guided and EfficientNet Feature Fusion for Down Syndrome Facial Screening

Down syndrome is associated with characteristic craniofacial features that have motivated the development of computer-vision-based facial-image-based screening systems. Recent studies have increasingly relied on deep computer vision and deep-learning approaches, but many provide limited interpretability, while earlier landmark-based methods offered transparent geometric and texture-based measurements. This creates a gap between interpretable handcrafted features and high-performing deep representations. To address this gap, this study proposes a hybrid interpretable–deep framework that combines landmark-derived geometry features, landmark-guided local binary pattern (LBP) texture descriptors, and frozen EfficientNetB0 convolutional neural network (CNN) deep features. The primary contribution of this study is the systematic integration and comprehensive evaluation of complementary interpretable and deep-feature representations within a unified facial-image screening framework. Feature fusion is followed by random forest feature ranking and SVM-RBF classification. Experiments were conducted on 2979 successfully processed facial images from an original dataset of 2999 images. Geometry-only, texture-enhanced, deep-feature, and hybrid fusion models were evaluated using repeated stratified train–test splits. The final RF Top-800 fusion model achieved strong facial-image classification screening performance, with F1 = 0.9045 +/− 0.0134 and AUC = 0.9675 +/− 0.0073 across repeated stratified train–test splits. Ablation analysis showed that removing geometry features, removing LBP features, or using only EfficientNetB0 reduced performance, supporting complementary contributions from interpretable geometry and texture feature components and deep-feature representations. Statistical comparisons and duplicate-sensitivity analyses further supported the robustness of the results. The findings demonstrate that landmark-derived geometry and texture descriptors remain valuable when integrated with modern deep representations, providing feature-level interpretability while improving screening performance through a hybrid framework that combines interpretable handcrafted features with high-performing deep representations.

Meshal Alfuraydi, H. Mathkour · Electronics · 0 citations

Review Open access Jul 2026

A Fairness Perspective on Client Selection and Aggregation Methods for Non-IID Mitigation in Federated Learning: A Survey

Federated learning (FL) is a promising approach for training distributed machine learning models while preserving clients’ data privacy. However, in real-world FL systems, data are often not independent and identically distributed (non-IID). This heterogeneity can slow convergence, degrade model performance, and increase client drift. To address these challenges, numerous methods have been proposed to mitigate non-IID data effects by optimizing client selection, local training, and model aggregation strategies. Despite their effectiveness in improving performance and efficiency, these methods rarely consider fairness across clients. Improving global accuracy does not guarantee balanced participation, influence, or outcomes, which may lead to biased model behavior across clients. In this survey, we review existing non-IID mitigation methods in FL from a fairness perspective and provide a systematic analysis of their implicit impact on client participation and influence. Unlike prior surveys that treat fairness as a separate research direction, this work analyzes how these methods designed for non-IID mitigation implicitly shape fairness outcomes across clients. Our taxonomy classifies existing methods into three categories—fairness-aware, semi-fairness-aware, and fairness-unaware—based on their design strategies for client selection and model aggregation. Using this taxonomy, we analyze the advantages, trade-offs, and limitations of each category and highlight that mitigating non-IID data does not guarantee fairness across clients. Finally, we identify open challenges and outline future directions, including system-level FL design that jointly considers non-IID mitigation and fairness and the development of standardized fairness evaluation metrics. Overall, this survey aims to provide a structured perspective on the relationship between non-IID mitigation and fairness and support the development of more balanced and scalable FL systems under non-IID conditions.

Mohannad Alsofyani, Isra Al-Turaiki, H. Mathkour · Electronics · 0 citations