(untitled) (Journal of Pharmaceutical Negative Results, 2022)
Aim: The objective of the work is to increase the precision of detecting objects using inventive Object localisation and Deep Convolutional Neural Networks with machine learning algorithms. Materials and Methods: The categorising is performed by adopting a sample size of n = 10 in Spatial Pyramid Pooling net in R-CNN and sample size n = 10 in R-CNN algorithms with a sample size = 10 and the G-Power analysis was carried out with 80% and the confidence interval 95%. Results and Discussion: The observation of the outcomes shows that the R-CNN using spatial pyramid pooling net layer has a high accuracy of 85.43% in comparison with the Region based convolutional neural network 78.36%. A statistically significant difference exists between the research groups with p=0.001 (2 tailed) (p<0.05). Conclusion: Detection of objects with high accuracy using machine learning shows that the spatial pyramid pooling net layer based R-CNN generates higher accuracy than region based convolutional neural network algorithms.