Mohamed

Aziz

Sakiss.

Real-time perception system using YOLOv11m trained on BDD100K. Integrated SAHI to detect small distant objects with 84% Precision.

Autonomous agent bridging WooCommerce and Dolibarr ERP. Features 'Tool Use' capabilities to perform real-time SQL inventory checks.

Actively engineering an end-to-end computer vision platform to monitor employee posture and workplace ergonomics. Architecture leverages MediaPipe for high-performance, real-time pose estimation.

Processed 1 Million+ transactions using PySpark. Solved class imbalance (8.7% fraud) using custom class weighting, achieving AUC > 0.99.

Developed a secure medical AI application that automates the classification of Brain Tumors (Glioma, Meningioma, Pituitary). The architecture utilizes a Parallel Ensemble Model, fusing features from VGG16 and ResNet50 backbones with a custom Channel Attention (SE) block for high diagnostic accuracy. Engineered a decentralized ledger (NeuroChain) where the model's prediction and the hash of the MRI image are instantly anchored on a blockchain to prevent data tampering. Features an end-to-end Streamlit workflow for real-time image upload, instant inference, and automatic blockchain transaction generation.

Embedded system deployed on Raspberry Pi 4. Uses a custom CNN (96% accuracy) combined with HSV filtering to eliminate false positives.