Computer engineer skilled in artificial intelligence and embedded systems. Passionate about applying technical and problem solving skills to develop innovative AI solutions for real-world challenges.
• Developing machine learning model for the ticketing system, including data preprocessing, feature engineering, and classification algorithms, and integrating an AI agent in n8n to automate ticket updates and system interactions.
• Building an automated analysis system in n8n to process data and generate actionable insights.
• Developed a new machine learning-based methodology for diagnosing heart sounds, achieving 98.44% accuracy in multi-class classification and 99.5% in binary classification, using custom-designed labeled datasets.
Data Preprocessing
Critical Thinking
Feature Engineering
Leadership
Troubleshooting
Communication skills
Developed an AI-powered heart sound diagnostic system on Raspberry Pi 4, integrating a microphone, amplifier, and ADC for real-time signal capture. Implemented STFT-based spectrogram preprocessing and fine-tuned a pretrained ResNet50 CNN in PyTorch to classify heart sounds as normal or abnormal, demonstrating skills in embedded systems, signal processing, and deep learning for accessible healthcare solutions.
Professional Accreditation Certificate – Saudi Council of Engineers (SCE).
Valid until: 07 January 2026
Python for Data Science, AI & Development - IBM.