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Image detection deep learning models are being used in various fields such as autonomous vehicles, medical, and agriculture. In the defense field, various efforts are being made to apply deep learning models to surveillance systems. Since the defense field deals with important issues such as combat, the reliability of the model is important. Therefore, this study intends to propose a new "strong supervised learning method" to ensure explainability of the defense image classification model. The proposed method is using the knowledge of military experts and the HITL (Human In The Loop) method in which humans and AI interact. This is the learning that provides the data of important parts of objects in addition to the general supervised learning which provides input data and labeling data. The learning process for the additionally provided data is carried out through a loss function of SSIM (Structural Similarity Index Map), which measures the similarity between data of important parts and a feature map of a convolutional neural network. In order to evaluate the proposed methodology, the model learned by general supervised learning and the model learned by the proposed method were compared. The comparison criteria were as follows: first, evaluation of similarity between the results of Grad CAM, a Visual Explain method, along with the data the important parts designated by experts; and second, the classification accuracy of objects. As a result of the experiment using the VGG16, ResNet50, InceptionV3, and Xception models, the similarity between the data of the Grad CAM and the region of interest was improved by an average of 15.52% based on the entire image area. In addition, the classification accuracy showed an average improvement of 4.72% based on Test Set. [ABSTRACT FROM AUTHOR]

The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling the identification of new therapeutic applications for existing drugs. In the present study, a trained deep-learning model was employed to screen a library of FDA-approved drugs to discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active and decoy compounds specific to JAK2 were obtained from the DUD-E database. RDKit, a cheminformatic toolkit, was utilized to extract molecular features from the compounds. The DeepChem framework's GraphConvMol, based on graph convolutional network models, was applied to build a predictive model using the DUD-E datasets. Subsequently, the trained deep-learning model was used to predict the JAK2 inhibitory potential of FDA-approved drugs. Based on these predictions, ribociclib, topiroxostat, amodiaquine, and gefitinib were identified as potential JAK2 inhibitors. Notably, several known JAK2 inhibitors demonstrated high potential according to the prediction results, validating the reliability of our prediction model. To further validate these findings and confirm their JAK2 inhibitory activity, molecular docking experiments were conducted using tofacitinib—an FDA-approved drug for JAK2 inhibition. Experimental validation successfully confirmed our computational analysis results by demonstrating that these novel drugs exhibited comparable inhibitory activity against JAK2 compared to tofacitinib. In conclusion, our study highlights how deep learning models can significantly enhance virtual screening efforts in drug discovery by efficiently identifying potential candidates for specific targets such as JAK2. These newly discovered drugs hold promises as novel JAK2 inhibitors deserving further exploration and investigation. [ABSTRACT FROM AUTHOR]