Abstract. In this paper, a new approach using ANFIS as a diagnosis system on WBCD problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and nonlinearity of the relationships between the large measured factors. It is possibly resolved with using AI algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data. Applying ANFIS as a diagnostic system was considered in our experiment. In addition, the computational performance of diagnosis system is an important issue as well as the output correctness of the inference system. Methods of using recommended inputs generated by the Genetic-Algorithm, Decision-Tree and Correlation-Coefficient computation with ANFIS was proposed to reduce the computational overhead.
We propose the VS3‐NET model to solve the task of question answering questions with machine‐reading comprehension that searches for an appropriate answer in a given context. VS3‐NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit‐based sentences and self‐matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question‐type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine‐reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS3‐NET model has an exact‐match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.
Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder-decoder model. The input document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder-decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder-decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00% and a label accuracy of 85.10% at the scene-level.
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