Realtime Visual Object Recognition using Support Vector Machine comparing with K- Nearest Neighbor algorithm for improving accuracy
Journal of Pharmaceutical Negative Results · 2022
Aim: The aim of this research is to recognise objects by using machine learning algorithms from the images with improved accuracy.Materials and Methods: A total of 104 samples are there for the two groups.Novel Support Vector Machine is considered as group 1 and K-Nearest Neighbor Algorithm is considered as group 2. Group 1 consists of 52 samples and Group 2 also consists of 52 samples and the G power is 80%.Results: The accuracy for the Novel Support Vector Machine algorithm (92%) is more than that of the K-Nearest Neighbor algorithm (83%).The mean accuracy detection is ±2SD and the significance value is 0.000 (p<0.01) which shows the hypothesis is correct and it is carried out using an independent sample T test.Conclusion: Hence, the accuracy of Novel Support Vector Machine is found to be 92% which is more than the accuracy of the K-Nearest Neighbor algorithm which is 83%.