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All Journal IAES International Journal of Robotics and Automation (IJRA)
Bougherara, Maamar
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Automotive Engineering Electrical & Electronics Engineering

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DEMAP: differential evolution mapping for network on chip optimization Bougherara, Maamar; Amara, Rafik; Kemcha, Rebiha
IAES International Journal of Robotics and Automation (IJRA) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v12i4.pp394-404

Abstract

Network-on-chip (NoC) is a new paradigm for system-on-chip (SoC) design, which facilitates the interconnection and integration of complex components. Since this technology is still new, significant research efforts are needed to accelerate and simplify the design phases. Mapping is a critical phase in the NoC design process, as a mismatch of application software components can significantly impact the final system's performance. Therefore, it is essential to develop automated tools and methods to ensure this step. The main objective of this project is to develop a new approach that can be used to map applications on the NoC architecture to reduce communication costs. To achieve this goal, we have opted for an optimization algorithm, specifically the differential evolution algorithm.
EdgeRetina: Hybrid multimedia architecture for diabetic retinopathy screening on low-cost mobiles Amina, Guidoum; Soltana, Achour; Bougherara, Maamar; Rafik, Amara; Tayeb, Mhamed
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp234-246

Abstract

Diabetic retinopathy (DR) is a major cause of preventable blindness, particularly in areas with limited medical resources where access to ophthalmologists is critical. Existing automated solutions struggle to balance clinical performance, cost-effectiveness, and robustness in the face of fundus image variability—including lighting differences, artifacts, and uneven capture quality. To address this challenge, we propose EdgeRetina, an integrated solution for diabetic retinopathy screening on low-cost mobiles. Our approach combines lightweight preprocessing (128×128 resizing, intensity normalization, and targeted augmentations simulating real-world conditions) with a hybrid SqueezeNet-MobileViT architecture (1.4 million parameters), optimized by dynamic threshold calibration (median: 0.3), maximizing clinical utility. Clinically calibrated INT8 quantization reduces the model to 8.27 MB (-92%) without altering diagnostic performance (sensitivity of 90.7% for referable diabetic retinopathies), while preserving compatibility with floating point 32 (FP32)-based gradient-weighted class activation mapping (Grad-CAM) visualizations. Evaluated on the APTOS 2019 dataset, this solution achieves an AUC of 0.96 with a latency (inference time) of 15.43 ms, reducing CPU consumption by 43% compared to FP32. The dynamic threshold/INT8 coupling decreases false positives by 71.4%. This pipeline thus enables accurate, accessible, and early screening of diabetic retinopathy on low-cost mobile devices, combining operational efficiency and diagnostic reliability in constrained environments, which is crucial to prevent avoidable blindness.
Multi-modal transformer and convolutional attention architectures for melanoma detection in dermoscopic images Amina, Guidoum; Bougherara, Maamar; Rafik, Amara
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp136-148

Abstract

The deadliest type of skin cancer, melanoma, requires early and accurate detection for a successful course of treatment. Traditional diagnostic techniques, which rely on visual inspection and dermoscopy, are frequently arbitrary and prone to human error. Automated melanoma detection exemplifies the integration of multimedia, a truly interdisciplinary field that melds visual data processing, human-computer interaction, and digital technologies. This study presents a multi-modal architecture: a multi-modal transformer network (MMTN) and a convolutional attention mechanism multi-modal (CAMM) that combines clinical data and dermoscopy images to enhance melanoma detection. The models achieve higher performance compared to other approaches by utilizing the strengths of architecture based on transformers, an encoder for image processing, dense layers for clinical data also Spatial Attention for the second architecture proposed. We evaluate the models on the entire set of ISIC 2019 data, showing significant improvements in accuracy and AUC. The models achieve high accuracy and AUC using CPU in both architectures. Our findings highlight the potential of a multi-modal learning architecture to enhance clinical decision-making and diagnostic accuracy in dermatology. To our knowledge, this is the first implementation combining MobileNet, transformer encoder attention, and clinical data fusion for the ISIC 2019 dataset, providing a significant advancement in the automated categorization of skin malignancies.
Co-Authors Amara, Rafik Amina, Guidoum Kemcha, Rebiha Rafik, Amara Soltana, Achour Tayeb, Mhamed