A fundamental problem facing deep neural networks is that they require a large amount of data to keep the system efficient in complex applications. Promising results of this problem are made possible by using techniques such as data enhancement or transfer learning in large data sets. However, when the application provides limited or unbalanced data, the problem persists. In addition, the number of false positives generated by deep model training has a significant negative impact on system performance. This study aims to solve the problem of false positives and class imbalances by implementing...
