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Explainable Graph Neural Network for Medical Science Research
Yewon Shin, Kisung Moon, Youngsuk Jung, Sunyoung Kwon
http://doi.org/10.5626/JOK.2022.49.11.999
Explainable AI (XAI) is a technology that provides explainability for our end-users to comprehend prediction results of ML algorithms. In particular, the reliability of the decision-making process of an AI algorithm through XAI technology is the most critical in the medical field in terms of real applications. However, complex interaction-based medical data restrict the application of existing XAI technologies developed mostly for image or text data. Graph Neural Network (GNN)-based XAI research has been highlighted in recent years because GNN is technically specialized to capture complex relationships in data. In this paper, we proposed a taxonomy according to the application method and algorithm of GNN-based XAI technology with current XAI research trends and its use-cases in four detailed areas of the medical field. We also expounded on the technical limitations and future works of XAI research specialized in the biomedical area.
A Product Review Summarization Considering Additional Information
Jaeyeun Yoon, Ig-hoon Lee, Sang-goo Lee
http://doi.org/10.5626/JOK.2020.47.2.180
Automatic document summarization is a task that generates the document in a suitable form from an existing document for a certain user or occasion. As use of the Internet increases, the various data including texts are exploding and the value of document summarization technology is growing. While the latest deep learning-based models show reliable performance in document summarization, the problem is that performance depends on the quantity and quality of the training data. For example, it is difficult to generate reliable summarization with existing models from the product review text of online shopping malls because of typing errors and grammatically wrong sentences. Online malls and portal web services are struggling to solve this problem. Thus, to generate an appropriate document summary in poor condition relative to quality and quantity of the product review learning data, this study proposes a model that generates product review summaries with additional information. We found through experiments that this model showed improved performances in terms of relevance and readability than the existing model for product review summaries.
Forecast of the Stock Market Price using Artificial Neural Network and Wavelet Transform
http://doi.org/10.5626/JOK.2019.46.12.1249
With advancements in technologies on machine learning and artificial neural network, various researches have attempted to predict the changes in the price of the stock market. The prediction accuracy has improved with adoption of new artificial neural network technologies that have been developed for image and voice signal processing. In the present work, the technical indices from KOSPI were decomposed for the prediction of index and movement direction of KOSPI into high-frequency part and low-frequency part using wavelet transform, then used to predict KOSPI independently by using artificial neural networks. For the final prediction, the prediction result of each frequency part was added. CNN, DPN, and LSTM were employed as artificial neural network; the performance of each model was compared and the efficiency of the wavelet transform of input variables was analyzed. CNN with 0.51% of MAPE for the index prediction and LSTM with 81.7% of accuracy for movement prediction showed the best performance among the three models. The efficiency of wavelet transform was confirmed with averaged 38% of the improved performance for the index prediction and averaged 25% of the improved performance for the movement prediction.
A Twitter News-Classification Scheme Using Semantic Enrichment of Word Features
Seonmi Ji, Jihoon Moon, Hyeonwoo Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2018.45.10.1045
Recently, with the popularity of Twitter as a news platform, many news articles are generated, and various kinds of information and opinions about them spread out very fast. But since an enormous amount of Twitter news is posted simultaneously, users have difficulty in selectively browsing for news related to their interests. So far, many works have been conducted on how to classify Twitter news using machine learning and deep learning. In general, conventional machine learning schemes show data sparsity and semantic gap problems, and deep learning schemes require a large amount of data. To solve these problems, in this paper, we propose a Twitter news-classification scheme using semantic enrichment of word features. Specifically, we first extract the features of Twitter news data using the Vector Space Model. Second, we enhance those features using DBpedia Spotlight. Finally, we construct a topic-classification model based on various machine learning techniques and demonstrate by experiments that our proposed model is more effective than other traditional methods.
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