Paper Title
Blood Group Detection via Finger Print Analysis on Raspberry PI

Abstract
When it comes to medical emergencies and diagnostics, blood group detection is very crucial. It may be time-consuming, resource-intensive, and inaccessible in rural places to conduct laboratory-based tests for blood type identification, which are the traditional approaches. The purpose of this research is to investigate a new method of blood group identification that is based on fingerprint analysis and is driven by machine learning. The method is implemented on a portable and inexpensive Raspberry Pi platform. The suggested method for capturing and preprocessing fingerprint photographs makes use of state-of-the-art image processing algorithms. Features that are associated with certain blood group traits are derived from fingerprint patterns. These characteristics are used to properly predict the person's blood type via a machine learning model that has been trained on a varied dataset. The system may be easily deployed in field contexts, rural healthcare clinics, and settings with limited resources thanks to the usage of Raspberry Pi, which allows for real-time processing and is portable. In comparison to more traditional approaches, the experimental findings show that fingerprint-based blood group identification is feasible and achieves comparable accuracy. The Raspberry Pi guarantees affordability and scalability, while the use of machine learning techniques guarantees resilience and flexibility. Keywords - Computer Vision, Image Processing, Fingerprint Analysis, Machine Learning, Blood Group Detection Medical Technology, Non-Invasive Diagnostics, Raspberry Pi, and Embedded Systems