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.