Disease analysis using machine learning approaches in healthcare system

This paper addresses congestion avoidance using enhanced blue algorithm (EBA) for data transferring in a network. The congestion of data always afects the data transmission on
the internet for various applications. For developing data transmission performance, the congestion of data is a challenging task. Although, diferent approaches have been used
to avoid data congestion, yet we have considered a data transmission framework for bet- ter performance compare to existing approaches. Thus, we considered the advanced Blue
Algorithm which is used to determine the node’s capacity with middle path and it prevents congestion by monitoring of data during data transmission. The role of gateway is consid-
ered to supervise status of congestion for both data sending and receiving based on positive or negative acknowledgment as well as data size. The gateway is also used for a congestion
notifcation system to alleviate congestion and enhance throughput. During experimen-tal analysis, we have taken comparative performance between existing and our proposed
model. For example, in Enhanced Ad hoc On-demand Distance Vector (EAODV), dur-ing the packet size of 10, the average end-to-end delay is 32.63 ms whereas in proposed advanced Blue algorithm, the average delay is only 19.11 ms. Thus, the proposed model using Blue algorithm is performed better than existing method.