Muhammad H is an AI Engineer with several years of experience in artificial intelligence, machine learning, and deep learning, specializing in healthcare applications. Proficient in technologies such as TensorFlow, Keras, OpenCV, and YOLO, he builds scalable AI systems for medical diagnostics and fraud detection. His expertise includes deploying models on AWS, Azure, and GCP, and he utilizes FastAPI and Flask for robust API development. He is skilled in MLOps, including CI/CD and containerization with Docker and…
Developed an advanced computer vision system using YOLO and OpenCV for real-time passenger counting in vehicles. Achieved 92% accuracy across 4 camera feeds, processing 1,247+ daily detections. Built analytics dashboard, multi-camera integration, and geographic mapping for traffic management. Deployed using Docker/FastAPI with 45ms processing time and 96.8% system accuracy.
Built a comprehensive deep learning pipeline for multi-disease diagnosis from medical images (X-rays, CT scans, MRI). Achieved 96% accuracy in pneumonia detection, 94% in brain tumor classification, and 91% in skin cancer detection. Implemented ensemble learning with ResNet-50, DenseNet-121, and custom CNNs. Developed web-based diagnostic tool with GRAD-CAM visualizations for explainable AI in healthcare applications.
Designed and implemented a high-performance real-time fraud detection system processing 10,000+ transactions per minute. Achieved 99.2% accuracy with <0.1% false positive rate using ensemble of XGBoost, Random Forest, and neural networks. Built streaming pipeline with Apache Kafka for real-time inference. Deployed complete MLOps pipeline with automated retraining, A/B testing, and monitoring using MLflow/Kubeflow.
Developed comprehensive NLP system for automated analysis of clinical notes, discharge summaries, and medical reports. Implemented medical NER achieving 95% F1-score, built sentiment analysis for patient outcome prediction (89% accuracy), and created automated ICD-10 classification system (92% accuracy) using BERT, GPT, and transformer models. Integrated multiple NLP pipelines for complete healthcare document processing.
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Muhammad H is an AI Engineer with several years of experience in artificial intelligence, machine learning, and deep learning, specializing in healthcare applications. Proficient in technologies such as TensorFlow, Keras, OpenCV, and YOLO, he builds scalable AI systems for medical diagnostics and fraud detection. His expertise includes deploying models on AWS, Azure, and GCP, and he utilizes FastAPI and Flask for robust API development. He is skilled in MLOps, including CI/CD and containerization with Docker and…
Developed an advanced computer vision system using YOLO and OpenCV for real-time passenger counting in vehicles. Achieved 92% accuracy across 4 camera feeds, processing 1,247+ daily detections. Built analytics dashboard, multi-camera integration, and geographic mapping for traffic management. Deployed using Docker/FastAPI with 45ms processing time and 96.8% system accuracy.
Built a comprehensive deep learning pipeline for multi-disease diagnosis from medical images (X-rays, CT scans, MRI). Achieved 96% accuracy in pneumonia detection, 94% in brain tumor classification, and 91% in skin cancer detection. Implemented ensemble learning with ResNet-50, DenseNet-121, and custom CNNs. Developed web-based diagnostic tool with GRAD-CAM visualizations for explainable AI in healthcare applications.
Designed and implemented a high-performance real-time fraud detection system processing 10,000+ transactions per minute. Achieved 99.2% accuracy with <0.1% false positive rate using ensemble of XGBoost, Random Forest, and neural networks. Built streaming pipeline with Apache Kafka for real-time inference. Deployed complete MLOps pipeline with automated retraining, A/B testing, and monitoring using MLflow/Kubeflow.
Developed comprehensive NLP system for automated analysis of clinical notes, discharge summaries, and medical reports. Implemented medical NER achieving 95% F1-score, built sentiment analysis for patient outcome prediction (89% accuracy), and created automated ICD-10 classification system (92% accuracy) using BERT, GPT, and transformer models. Integrated multiple NLP pipelines for complete healthcare document processing.
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