The aim of this research work is to compare the accuracy of Adaboost algorithm with K-Nearest Neighbours for the density-based smart traffic system. The dataset named Smart City Traffic Patterns consists of 360,000 images considered for the application. Two groups were selected for this investigation, and Adaboost and K-Nearest Neighbours were the algorithms employed. With g power setting values of 0.05 and 0.85, the test’s average Gpower is roughly 85%. The analysis of the traffic system has been done using Adaboost and K-Nearest Neighbours. It shows that there is a statistical difference between adaboost and K-Nearest Neighbours with p=0.013 (the independent sample T-test p<0.05). The proposed method demonstrated strong performance with a mean accuracy of 93%, exceeding the conventional method’s 90% accuracy. When compared to K-Nearest Neighbours for the density-based smart traffic system, Adaboost has higher accuracy.
Topics
Artificial intelligence
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