• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.11-20
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• 2024

This study presents TabNet, a novel deep learning method, to enhance corporate credit rating accuracy amidst growing financial market uncertainties due to technological advancements. By analyzing data from major Korean stock markets, the research constructs a credit rating prediction model using TabNet. Comparing it with traditional machine learning, TabNet proves superior, achieving a Precision of 0.884 and an F1 score of 0.895. It notably reduces misclassification of high-risk companies as low-risk, emphasizing its potential as a vital tool for financial institutions in credit risk management and decision-making.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.453-455
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• 2023

본 연구는 은행에서 리스크 관리 자동화를 위해 고객의 대출 상환 여부 예측 모델을 제안하고자 한다. 예측 모델로 금융 데이터 같은 정형데이터에서 전통적으로 높은 성능을 보인 의사결정나무기반 모델 LightGBM, CatBoost, XGB 와 최근 제안된 정형데이터에서 사용할 수 있는 설명 가능한 딥러닝 기반 모델 TabNet 간의 성능 비교를 진행한다. 다만, 대출 상환 여부 데이터는 불균형 클래스 데이터로 구성되어있어 샘플링을 진행한다. SMOTE, Random Under Sampling, 혼합 방식을 비교해 가장 높은 성능의 샘플링 기법을 제안한다. 대출 상환 여부 예측 결과 TabNet 모델이 의사결정나무모델들보다 좋은 성능을 보여 정형데이터에서 의사결정나무 기반 모델을 딥러닝 모델이 대체 할 수 있는 가능성을 확인했다.

• Smart Media Journal
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• v.11 no.2
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.119-124
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• 2023

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.84-87
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• 2021

본 논문에서는 딥러닝을 기반으로 문서영상에서 표 안의 셀 경계선을 히트맵 회귀(heatmap regression)로 추정함으로써 표의 구조를 인식하는 방법을 제안한다. 표는 기본적으로 행과 열로 이루어져 있기 때문에, 제안하는 방법에서는 먼저 1 차원 벡터 형태로 세로/가로 방향의 행/열 경계선 위치를 찾고, 이에 병합된 셀을 처리하기 위해 경계선이 그어져야 할 위치를 2 차원으로 추정한 결과를 적용하여 온전한 표의 경계선을 구한다. 이러한 구조를 통해 제안하는 방법은 표의 행과 열에 대한 정보를 효과적으로 이용함과 동시에, 복잡한 후처리 없이 병합된 셀을 처리할 수 있는 이점을 보인다. 실험은 1 차원의 행/열 경계선 위치를 반영하는 두 가지 방식에 대해 PubTabNet[11]에 대해 진행하여 결과를 보였다.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• Journal of the Korea Organic Resources Recycling Association
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• v.13 no.2
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• pp.98-106
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• 2005

Population dynamics in the vermicomposting bed was investigated. And harvesting efficiencies of earthworm attracting traps with different attractants, mesh sizes of net and covering materials was evaluated. Peak density of earthworm population was $5kg/m^2$ and therafter its desity kept $4.4{\sim}5.0kg/m^2$. It was evaluated that suitable mesh size of the net was 7mm and proper covering materials of the earthworm attracting trap was cotton quilt. The earthworm harvesting efficiency of trap with attractant was much higher than that of trap without attractant. With more amount of attractant in the trap and with longer setting period of trap onto the vermicomposting bed, the earthworm attracting trap harvested more earthworms. Crushed pear-peel attracted much more earthworms than paper mill sludge, but it was difficult to supply enough amount of crushed pear-peel for practical need. Sugar solution(10%) with tab water was proven to be an alternative to crushed pear-peel because its attracting effect on earthworm was as high as crushed pear-peel and it was easy to prepare and supply in large quantity.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.