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@article{169714,
        author = {Shree Shivani.D.V and Santhiya.G and Priyanka.K and Subhashni.B and Soundarya.E},
        title = {Blood Group Prediction Using Thumb Fingerprint With Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2095-2100},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169714},
        abstract = {Determining blood type accurately and quickly is crucial, especially in emergency situations such as accidents or surgeries where immediate blood transfusion may be required. Traditionally, blood typing is carried out manually by trained technicians, a process that is time-consuming and prone to human error. 
This project aims to develop a highly efficient and accurate system for blood group prediction using pre-acquired palm images obtained through advanced imaging sensors. 
After preprocessing, feature extraction techniques are applied to identify unique patterns in the palm images that correlate with different blood types. These extracted features are critical, as they form the basis for distinguishing between various blood groups. The system then leverages machine learning algorithms Convolutional Neural Networks to analyze these features.
By integrating machine learning with advanced biometric techniques, this project contributes to the growing field of healthcare technology, offering a novel solution that has the potential to improve the speed and accuracy of blood typing in critical medical situations.},
        keywords = {Fingerprint images, convolution neural networks, autoencoder, feature extraction, system identification.},
        month = {November},
        }