Aayush User

Implemented semantic land segmentation using the Se,NB architecture in PyTorch for flexibility and control. Developed an encoder-decoder structure with skip connections, suitable for pixel-wise binary segmentation.

Created a disease prediction system based on user-provided symptoms, utilizing a machine learning model trained on a Kaggle dataset. Provided a user-friendly interface for symptom input and disease predictions, including features like user accounts and chat functionality.

Developed an attendance tracking system using facial recognition, enabling real-time attendance management. Implemented user registration and login systems, along with robust data storage and retrieval for attendance records.

Activities and societies: Attended a tracking using facial recognition, real-time attendance management, user registration and login system, and attendance data storage and retrieval.

Studied the fundamentals of computer science including programming languages, data structures, and algorithms. Gained expertise in developing real-world projects, especially in Python and machine learning technologies to solve practical problems. A quick learner, strong collaborator, and continuously expanding knowledge in development and AI systems.

Developed a machine learning model trained on a dataset from Kaggle. Provided a user interface for users to input their symptoms and receive disease predictions. Included features such as user accounts, chat functionality, and disease prediction.

Implemented in PyTorch for flexibility and control. Used an encoder-decoder structure with skip connections. Suitable for pixel-wise binary segmentation. Evaluation metrics: Dice Coefficient, Intersection Over Union (IoU), and Pixel Accuracy.