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Almost a decade ago, we could all have heard of the smart card and its role in the financial sector, especially for retail transactions. The smart card provides special security compared to normal monetary transactions. But its implementation in the case of retail payments has not been as popular. But it was really useful in the area of transit payments and personal identification. But some other sectors, such as mobile telecommunications, found this more useful and began using them in mobile phones as subscriber identification modules (SIMs). But in today's world, people need to carry different cards like PAN cards, driver's licenses, Aadhaar cards, etc., which makes people more uncomfortable as they have to carry different cards at the same time.

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The detection of liver cancer in its early stages is very difficult. The proposed system collects microscopic images as input from the patients and preprocesses them to extract features. Once the feature extraction stage is completed the classification of the image need to be done on them. The proposed system uses the probabilistic Neural Networks (PNN), K-nearest neighbor (KNN), and Learning Vector Quantization (LVQ) classifiers to classify the images into their respective classes. The classifier system uses the normal approach of classification i.e., a normally two stages one is training and then testing. Each of these classifiers goes through both these stages. Firstly, the training stage involves the system learning on the images and their respective category which is already known from the expert advice. In this way a series of images are given in the form of an input with their actual category. The classifier learns from this and then in the testing phase a new image is given for classification to the system. The system uses the prior knowledge which it has learnt during the training phase to predict the category for the image. The accuracy of each classifier is being calculated to apply the classifier with more accuracy for the prediction of the disease.