Ad hoc networks are suitable for wide range applications like battlefields, analyzing traffic and during disaster recovery operations. With the development of Vehicular Ad hoc Networks the transmission of emergency messages is considerably progressed to accelerate traffic safety measures. As vehicles communicate with each other by connecting through Internet and by using Road Side Units (RSI), there will be each and every possibility to transmit fake messages by accessing VANET Server by unfair means. This requires attention of filtering of messages before transmission over the network through the Server. In this paper, the proposed approach provides a magnificent solution in identifying and obstructing fake messages before passing through the Server. This leads to attempt a contemporary approach in blocking fake messages that helps in causing unwanted disturbance to the traffic instantly. In addition to that the intended process gives a clear analysis in storing of the sender along with the timely analysis of message transmission for tracking source. Obviously this outperforms in trusting the alerts of post-crash notifications and reducing security issues in VANETs

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.