• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1331-1336
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• 2023

In text-based fish disease classification using machine learning, there is a problem that the input parameters of the machine learning model are too many, but due to performance problems, the input parameters cannot be arbitrarily reduced. This paper proposes a method of optimizing input parameters specialized for Paralichthys olivaceus disease classification using SHAP analysis techniques to solve this problem,. The proposed method includes data preprocessing of disease information extracted from the halibut disease questionnaire by applying the SHAP analysis technique and evaluating a machine learning model using AutoML. Through this, the performance of the input parameters of AutoML is evaluated and the optimal input parameter combination is derived. In this study, the proposed method is expected to be able to maintain the existing performance while reducing the number of input parameters required, which will contribute to enhancing the efficiency and practicality of text-based Paralichthys olivaceus disease classification.

• Journal of Science Education
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• v.47 no.1
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• pp.37-51
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• 2023

This study explored the factors influencing major satisfaction and academic satisfaction of science education major students at the College of Education using machine learning models, random forest, gradient boosting model, and SHAP. Analysis results showed that the performance of the gradient boosting model was better than that of the random forest, but the difference was not large. Factors influencing major satisfaction include 'satisfaction with science teachers in high school corresponding to the subject of one's major', 'motivation for teaching job', and 'age'. Through the SHAP value, the influence of variables was identified, and the results were derived for the group as a whole and for individual analysis. The comprehensive and individual results could be complementary with each other. Based on the research results, implications for ways to support pre-service science teachers' major and academic satisfaction were proposed.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.5
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• pp.51-68
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• 2024

To safely operate lithium-ion batteries that power mobile electronic devices, it is crucial to accurately predict the remaining useful life (RUL) of the battery. Recently, with the advancement of machine learning technologies, artificial intelligence (AI)-based RUL prediction models for batteries have been actively researched. However, existing models have limitations as the reasoning process within the models is not transparent, making it difficult to fully trust and utilize the predicted values derived from machine learning. To address this issue, various explainable AI techniques have been proposed, but these techniques typically visualize results in the form of graphs, requiring users to manually analyze the graphs. In this paper, we propose an explainable RUL prediction method for lithium-ion batteries that interprets the reasoning process of the prediction model in textual form using SHAP analysis based on large language models (LLMs). Experimental results using publicly available lithium-ion battery datasets demonstrated that the LLM-based SHAP analysis enabled us to concretely understand the model's prediction rationale in textual form.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.177-184
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• 2024

In the situation of increasing data at fast speed, we use all kinds of complex ensemble and deep learning algorithms to get the highest accuracy. It's sometimes questionable how these models predict, classify, recognize, and track unknown data. Accomplishing this technique and more has been and would be the goal of intensive research and development in the data science community. A variety of reasons, such as lack of data, imbalanced data, biased data can impact the decision rendered by the learning models. Many models are gaining traction for such interpretations. Now, LIME and SHAP are commonly used, in which are two state of the art open source explainable techniques. However, their outputs represent some different results. In this context, this study introduces a coupling technique of LIME and Shap, and demonstrates analysis possibilities on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence on the IEEE CIS dataset.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.53-58
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• 2023

With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a "black box" model, Shapley values help us to alleviate the "black box" problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses K-nearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.

• The Journal of Bigdata
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• v.5 no.2
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• pp.215-229
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• 2020

The interest in machine learning is growing in all industries, but it is difficult to apply it to real-world tasks because of inexplicability. This paper introduces a case of developing a financial customer churn prediction model for a securities company, and introduces the research results on an attempt to develop a machine learning model that can be explained using the SHAP Value methodology and derivation of interpretability. In this study, a total of six customer churn models are compared and analyzed, and the cause of customer churn is inferred through the classification and data analysis of SHAP Value and the type of customer asset change. Based on the results of this study, it would be possible to use it as a basis for comprehensive judgment, such as using the Value of the deviation prediction result that can infer the cause of the marketing manager's actual customer marketing in the future and establishing a target marketing strategy for each customer.

• Annual Conference of KIPS
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• 2024.05a
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• pp.768-771
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• 2024

인간의 산업 활동으로 인하여 동물들의 생존이 위협받으면서, 동물의 서식 분포를 효과적으로 파악할 수 있는 자동 야생동물 모니터링 기술의 필요성이 점점 더 커지고 있다. 그중에서도 동물 소리 분류 기술은 시각적으로 식별이 어려운 동물에게도 효과적으로 적용할 수 있는 장점으로 인하여 널리 사용되고 있다. 최근 심층학습 기반의 분류 모델들이 좋은 판별 성능을 보여주고 있어 동물 소리 분류에 많이 사용되고 있지만, 희귀종과 같이 개체 수가 적어 데이터가 부족한 경우에는 학습이 제대로 이루어지지 않을 수 있다. 또한, 이러한 모델들은 모델 내부에서 일어나는 추론 과정을 알 수 없어 결과를 완전히 신뢰하고 사용하는 데 제약이 따른다. 이에 본 논문에서는 전이 학습을 통해 데이터 부족 문제를 고려하고, SHAP을 이용하여 분류 모델의 추론 과정을 해석하는 설명가능한 동물 소리 분류 기법을 제안한다. 실험 결과, 제안하는 기법은 지도 학습을 한 경우보다 분류 성능이 향상됨을 확인하였으며, SHAP 분석을 통해 모델의 분류 근거를 이해할 수 있었다.

• Annual Conference of KIPS
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• 2023.05a
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• pp.370-372
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• 2023

신용카드 부정 사용은 고객 및 기업의 신용과 재산에 막대한 손실을 미치고 있다. 이에 따라 금융사들은 이상금융거래탐지시스템을 도입하였으나 이상 거래 발생 여부를 지속적으로 모니터링하고 있기 때문에 시스템 유지에 많은 비용이 따른다. 따라서 본 논문에서는 컴퓨팅 리소스를 절약함과 동시에 성능 개선 효과를 보인 신용카드 이상 거래 탐지 알고리즘을 제안한다. CTGAN 을 활용하여 정상 거래와 이상 거래의 비율을 일부 완화하였고 XAI 기법인 SHAP 를 활용하여 유의미한 속성값을 선택하였다. 이것을 기반으로 LSTM Autoencoder를 사용하여 이상데이터를 탐지하였다. 그 결과 전통적인 비지도 학습 기법에 비해 제안 알고리즘이 우수한 성능을 보였음을 확인하였다.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Annual Conference of KIPS
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• 2023.05a
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• pp.706-708
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• 2023

In this study, we embark on a journey to uncover the essence of emotions by exploring the depths of transfer learning on three pre-trained transformer models. Our quest to classify five emotions culminates in discovering the KLUE (Korean Language Understanding Evaluation)-BERT (Bidirectional Encoder Representations from Transformers) model, which is the most exceptional among its peers. Our analysis of F1 scores attests to its superior learning and generalization abilities on the experimental data. To delve deeper into the mystery behind its success, we employ the powerful SHAP (Shapley Additive Explanations) method to unravel the intricacies of the KLUE-BERT model. The findings of our investigation are presented with a mesmerizing text plot visualization, which serves as a window into the model's soul. This approach enables us to grasp the impact of individual tokens on emotion classification and provides irrefutable, visually appealing evidence to support the predictions of the KLUE-BERT model.