Education acts as a soul in the overall societal development, in one way or the other. Aspirants, who gain their degrees genuinely, will help society with their knowledge and skills. But, on the other side of the coin, the problem of fake certificates is alarming and worrying. It has been prevalent in different forms from paper-based dummy certificates to replicas backed with database tampering and has increased to astronomic levels in this digital era. In this regard, an overlay mechanism using blockchain technology is proposed to store the genuine certificates in digital form and verify them firmly whenever needed without delay. The proposed system makes sure that the certificates, once verified, can be present online in an immutable form for further reference and provides a tamper-proof concealment to the existing certification system. To confirm the credibility of the proposed method, a prototype of blockchain-based credential securing and verification system is developed in ethereum test network. The implementation and test results show that it is a secure and feasible solution to online credential management system.
It is necessary to preserve information about the individuals in many organizations. So this paper is introduced to implement a distributed anonymization protocol to store data about the users on horizontally partitioned databases by providing secure query protocol to share data according to the specified query from the virtual databases. It concentrates on architecture querying heterogeneous distributed and also private databases.
Inflammatory syndrome usually occurs with the adults who have already affected with corona virus. Multi system syndrome in children may affect different parts of the body like lungs, liver, kidneys and brain. The level of current infection can be known by proceeding with two kinds of tests, namely viral tests and anti body tests. Viral tests determine the level of current infection and antibody tests are about past infection. Though decision on test will be provided by health care units, there will be an uncertainty in evaluation of results. The variation in potential of antibodies will influence the resulting parameters. The effect of inaccurate test results is to be traced, because people may suffer from reinfection in their bodies over a period of time. In this paper the uncertainty analysis is carried out using Cubist and OneR algorithms. In the cubist algorithm leaf nodes can be analyzed using regression models. Accuracy of the predictions are analyzed using contingency table and false positives and true negatives are tracked using confusion matrix. This analysis assists in determining the trans conditional state of the disease with dynamism. With the analysis carried out cubist proves to be the best algorithm.
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