Object classification based-on patterns using random forest classifier compared with enhanced K-nearest neighbor algorithm Available to Purchase

This article’s main objectives are to recognize the sequence in the image, distinguish the object it is, and analyse it correctly. Using the nearest neighbor classifier and the novel random forest classifier, the input picture is used to predict the image recognition. The Kaggle database served as the source of the study dataset for this examination. larger accuracy was predicted for visual pattern analysis (with a sample size of 10 from G1 and 10 from G2) with a sample size of 20. The computation involved the use of a 95% poise interval, an alpha and beta value of 0.2 and 0.05, and a G-power of 0.8. With 91.54 percent exactness, the suggested novel RF outperforms the latter, which has an exactness pace of 85.33 percent. p = 0.001 (Independent Sample T Test p = 0.05) indicates the statistical significance of the difference between the two algorithms. Data analysis shows that for image pattern recognition, the novel random forest model that has been proposed performs better than the K nearest neighbor algorithm.