The emergence of BERT and Knowledge Graph (KG) has promoted the development of Question Answering (QA), however, existing QA systems are still inadequate in terms of the accuracy of relational reasoning and the interpretability of answers. In this paper, we combine BERT and KG, following and optimizing existing methods to build a medical QA system with better performance - KBMQA. The final experimental results show that KBMQA performs better on both MedQA and MedNLI datasets compared with previous biomedical baseline models and MOP models.
Poisoned by the edible fungus accident occurred frequently in recent years since that there were no effective and quick recognition methods for the wild fungus. To tackle the problem, a wild fungus classification algorithm based on a deep convolutional neural network (CNN) and Residual Network (ResNet), is proposed in this paper. An optimization method is also proposed for network training. In order to verify the effectiveness of the model and optimization method, a wild fungus database, in total of 1280 images, is used in this paper. The experimental results show that the proposed algorithm can effectively complete the classification task of wild mushrooms, and the optimization algorithm proposed in this paper can also effectively improve the classification effect of the algorithm model.
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