Machine learning is a subfield of artificial intelligence which defines the capability of a machine to imitate intelligent human behavior. It is a part of artificial intelligence where computer algorithm improves congestion control of TCP in wired/wireless networks. Decision tree boosting is best method for application appear .the high flexibility, adaptability provides extends traditional approach usedin multiple fields including network operation and management based on Congestion Control in Computer Networks using Machine Learning Approach
In machine learning we used decision tree based algorithm for providing usual result for classify the problems. To make a reliable estimate we randomly need to divide the database into two divisions that consist of learning sample as one part and its ROC curve, AUC, and error rate are evaluated on the validation sample. The main criteria to evaluate this protocol which are bandwidth usage in the case of wireless links and TCP in case of the wired network. There RL-based CC algorithm are much feasible on NS3 simulator which separates the calculation on simulation result and analysis. Thus by the decision tree the machine learning algorithm the research process will be proposed.
PhD in Congestion Control in Computer Networks using Machine Learning Approach
A way to increase the throughput of TCP over wireless links is to prevent it from reducing its rate when it faces a loss due to a link error.
There still a possible computational efficiency requires to consider the impact of reduction of accuracy on behavior of the protocol.
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