ABDELKADER ADDA AMAR@Belarar
2
Events attended
2
Submissions made
Algeria
1 year of experience
About me
"A Biological Sciences Researcher specialized in Parasitology and a Mobile App Developer passionate about integrating AI into medical diagnostics. I focus on developing real-time, offline solutions using Computer Vision (YOLOv8/v11) to improve parasite detection accuracy in laboratory settings. Recently recognized as a Top Builder on Lablab.ai for the DAP (Diagnostic AI for Parasitology) project, which achieved high performance in technology application and innovation. I am dedicated to bridging the gap between clinical biology and artificial intelligence to create impactful healthcare tools."
🤓 Latest Submissions
PULSE Platform
Long Description (الملخص الطويل المختصر - 180 كلمة) > **PULSE (Real-time Health Monitoring Platform) is an integrated digital health surveillance system that bridges the gap between field diagnostics and centralized data. It connects the DAP mobile app—which uses embedded AI to identify parasite species from microscope images—with a live, web-based epidemiological monitoring platform.** > **To ensure absolute patient confidentiality, PULSE implements strict privacy-first protocols, including SHA-256 cryptographic hashing for patient IDs and location fuzzing (±500m random offset) before storing data in a secure PostgreSQL database.** > **The dual-language (Arabic + English) web platform provides health authorities with an interactive dashboard that auto-refreshes every 2 minutes. It features live map visualizations, color-coded disease markers, and smart outbreak alerts when daily cases cross defined thresholds. Furthermore, it uses linear regression models on 30-day historical data to provide 7-day future epidemic forecasting.** > **A key innovation is its automated WebData Integration via Celery, which automatically scrapes and aggregates global infectious disease alerts from the WHO and ProMED-mail every 6 hours. PULSE empowers health agencies to move from reactive measures to proactive epidemic management.*
31 May 2026
DAP: AI-Powered Parasitology System
DAP is an innovative project that bridges Biological Sciences and Artificial Intelligence. We developed a mobile application using the YOLOv8 model, achieving 80% accuracy in detecting various parasites, including Leishmania and Malaria, directly from microscopic images. Our solution is designed for remote and rural areas where internet access is limited and expert microscopists are scarce. The app works entirely offline to provide immediate results.Furthermore, the system captures GPS coordinates for each positive diagnosis. Once a connection is established, this data is synchronized to a central dashboard. This creates a "Big Data" epidemiological map, allowing health organizations to monitor disease outbreaks in real-time and distribute medical resources efficiently. Our goal is to digitize parasitology and save lives through early detection and precise mapping.**
10 May 2026
