In this paper, we represent a method for machine learning to predict the defect in foundry operation. Foundry has become a driving tool to produce the part to another industry like automobile, marine, and weapon. These foundry processes mainly have two critical problems to decrease the quality assurance. Now, we have to predict the defect to increase the quality of foundry operation. The foundry process’s failure is associated with micro shrinkage and ultimate tensile strength. We process by utilizing a machine learning classifier to predict the micro shrinkage and maximum tensile strength and describe the process, learning process, and evaluate the predataset from the foundry process to compare the accuracy and stability.
Traditional skin cancer screening necessitates a time-consuming physical examination by a dermatologist. One of the most difficult tasks in image analysis is automated medical picture recognition and categorization. Making a skin-based analysis to predict the abnormalities present in a particular area is always a competitive issue. In the case of automatic region identification, deep learning (DL) methodologies play a major role. Most DL approaches are unable to offer uncertainty quantification (UQ) for the output, resulting in overconfidence and excessive sample deviation. The dataset was contributed by the International Skin Image Collaboration (ISIC) Archive. To deal with uncertainty during skin cancer image classification and disappearing gradients, we used Monte-Carlo (MC) dropout with the ResNet 50 DL model. This assists in overcoming the problem of vanishing gradients. After the preprocessing, the training was completed, yielding a 94% accuracy rate. The MC dropout method identifies the out-of-domain data, reduces the training size which results in an accuracy of 89.9%. As a result, the ResNet50 model for classifying skin cancer achieved 89% of accuracy. In addition, our model was in general robust to noise and performed well with previously unseen data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.