MUHAMMED ADIL

Led the development of a blockchain-powered platform utilizing Django and smart contracts to automate and secure the car scrapping process, improving transaction transparency and efficiency. Designed and deployed RESTful APIs using Django Rest Framework for seamless data integration and user management across the platform. Integrated machine learning models (TensorFlow) and advanced computer vision to automate car damage detection, achieving high accuracy in assessments. Applied Agile methodologies and Git-based version control to coordinate cross-functional collaboration and ensure timely project delivery.

Led the development of a blockchain-powered platform utilizing Django and smart contracts to automate and secure the car scrapping process, improving transaction transparency and efficiency. Designed and deployed RESTful APIs with Django Rest Framework for data integration and user management across the platform. Integrated machine learning models (TensorFlow) and advanced computer vision to automate car damage detection, achieving high accuracy in assessments. Applied Agile methodologies and Git-based version control to coordinate cross-functional collaboration and ensure timely project delivery.

Designed and implemented a deep convolutional neural network (CNN) using TensorFlow and Keras to classify multiple crop leaf diseases with 90% accuracy. Automated large-scale image preprocessing, augmentation, and analysis by leveraging Pandas and NumPy, enhancing the reliability of diagnosis. Reduced manual diagnosis time and improved farm productivity by providing AI-driven disease insights and actionable recommendations. Developed robust visualization tools with Matplotlib to monitor model performance and support data-driven decision-making. Employed test-driven development and unit testing to ensure solution dependability and scalability.