• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.125-131
• /
• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• Journal of Korean Society for Quality Management
• /
• v.40 no.1
• /
• pp.39-48
• /
• 2012

An early detection system for warranty issues periodically collects customers' claim data and automatically reports alarms about emerging issues based on statistical algorithms. It helps companies to reduce an issue definition time and save the handling cost of warranty claims. This paper provides an evaluation framework to validate the economic effect of an early detection system project. For this purpose, we present economical index of a project with explicit formulas such as ROI(return on investment), PP(payback period), NPV(net present value), PI(profitability index) and IRR(internal rate of return) and analyze the sensitivities of the index according to the variation of project input parameters. The proposed analysis framework is expected to be used for evaluating economic values of various system integration projects.

• Journal of The Korean Astronomical Society
• /
• v.41 no.4
• /
• pp.99-107
• /
• 2008

When a new counting experiment is proposed, it is crucial to predict whether the desired source signal will be detected, or how much observation time is required in order to detect the signal at a certain significance level. The concept of the a priori prediction of the detection limit in a newly proposed experiment should be distinguished from the a posteriori claim or decision whether a source signal was detected in an experiment already performed, and the calculation of statistical significance of a measured source signal. We formulate precise definitions of these concepts based on the statistical theory of hypothesis testing, and derive an approximate formula to estimate quickly the a priori detection limit of expected Poissonian source signals. A more accurate algorithm for calculating the detection limits in a counting experiment is also proposed. The formula and the proposed algorithm may be used for the estimation of required integration or observation time in proposals of new experiments. Applications include the calculation of integration time required for the detection of faint emission lines in a newly proposed spectroscopic observation, and the detection of faint sources in a new imaging observation. We apply the results to the calculation of observation time required to claim the detection of the surface thermal emission from neutron stars with two virtual instruments.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.413-423
• /
• 2022

The NHIS provides free or highly subsidized healthcare to all people by providing financial fortification. However, the financial sustainability of the scheme is threatened by numerous factors. Therefore, this work sought to provide a solution to process claims intelligently. The provided Petri net model demonstrated successful data flow among the various participant. For efficiency, scalability, and performance two main subsystems were modelled and integrated - data input and claims processing subsystems. We provided smart claims processing algorithm that has a simple and efficient error detection method. The complexity of the main algorithm is good but that of the error detection is excellent when compared to literature. Performance indicates that the model output is reachable from input and the token delivery rate is promising.

• Annual Conference on Human and Language Technology
• /
• 2023.10a
• /
• pp.234-238
• /
• 2023

미세 조정은 대부분의 연구에서 사전학습 모델을 위한 표준 기법으로 활용되고 있으나, 최근 초거대 모델의 등장과 환경 오염 등의 문제로 인해 더 효율적인 사전학습 모델 활용 방법이 요구되고 있다. 패턴 추출 학습은 사전학습 모델을 효율적으로 활용하기 위해 제안된 방법으로, 본 논문에서는 한국어 주장 탐지 및 입장 분류를 위해 패턴 추출 학습을 활용하는 모델을 구현하였다. 우리는 기존 미세 조정 방식 모델과의 비교 실험을 통해 본 논문에서 구현한 한국어 주장 탐지 및 입장 분류 모델이 사전학습 단계에서 학습한 모델의 내부 지식을 효과적으로 활용할 수 있음을 보였다.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.2
• /
• pp.147-156
• /
• 2013

In this paper, a new speech endpoint detection method in noisy environments for moving robot platforms is proposed. In the conventional method, the endpoint of speech is obtained by applying an edge detection filter that finds abrupt changes in the feature domain. However, since the feature of the frame energy is unstable in such noisy environments, it is difficult to accurately find the endpoint of speech. Therefore, a novel feature extraction method based on the twice-iterated fast fourier transform (TIFFT) and statistical models of speech is proposed. The proposed feature extraction method was applied to an edge detection filter for effective detection of the endpoint of speech. Representative experiments claim that there was a substantial improvement over the conventional method.

• Journal of Information Technology Services
• /
• v.8 no.4
• /
• pp.175-185
• /
• 2009

Objectifying claims filed during the warranty period, analyzing the current circumstances and improving on the problem in question is an activity worth doing that could reduce the likelihood of claims to occur, cut down on the costs, and enhance the corporate image of the manufacturer. Existing analyses of claims are confronted with two problems. First, you can't precisely assess the risks of claims involved by means of the value of claims per 100 products alone. Second, even in a normal state, the existing approach fails to capture the probabilistic conflicts that escape the upper control limit of claims, thus leading to wrong control activities. To solve the first problem, this paper proposed that a time series detection concept where the claim rate is monitored based on the date when problems are processed and a hazard function for expression of the claim rate be utilized. For the second problem, this paper designed a model whereby to define a normal state by making use of PID (Proportion, Integral, Differential) and infer by way of a fuzzy concept. This paper confirmed the validity and applicability of the proposed approach by applying methods suggested in the actual past data of warranty claims of a large-scaled automotive firm, unlike hypothetical simulation data, in order to apply them directly in industrial job sites, as well as making theoretical suggestions for analysis of claims.

• 한국IT서비스학회:학술대회논문집
• /
• 2009.05a
• /
• pp.357-360
• /
• 2009

본 연구는 데이터마이닝 기법을 이용하여 건강보험청구료에 있어서 이상정도가 심한 요양기관을 탐지하고, 실제 의료영역에 적용하기 위한 시스템 개발을 목적으로 한다. 현재 건강보험 심사평가원의 이상탐지시스템은 평가대상이 되는 항목을 개별적으로 평가하고, 탐지된 기관의 선정 이유에 대한 근거제시가 부족한 단점을 가지고 있다. 따라서 본 연구에서는 항목을 종합적으로 평가할 수 있는 정량적 지표를 설계하고, 항목들의 상대적 중요도를 파악할 수 있도록 항목들에 대한 가중치 부여한다. 또한 지표에서 얻어진 값으로 등급을 구분하고, 의사결정나무기법(decision tree)를 이용하여 해석력을 높이는 방법을 제시한다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.3
• /
• pp.553-563
• /
• 1996

Detecting edges is one of issues with essentialimprotance in the area of image analysis. An edge in an image is a boundary or contour at which a significant change occurs in image intensity. Edge detection has been studied in many addlications such as imagesegmentation, robot vision, and image compression. In this paper, we propose an automatic threshold selection scheme for edge detection and show its application to noise elimination. The scheme suggested here applied statistical properties of the noise estimated from a noisy image to threshold selection. Since a selected threshold value in the scheme depends on not the characgreistic of an orginal image but the statistical feature of added noise, we can remove ad-hoc manners used for selecting the threshold value as well as decide the value theoretically. Furthermore, that shceme can reduce the number of edge pixels either generated or lost by noise. an application of the scheme to noise elimination is shown here. Noise in the input image can be eliminated with considering the direction of each edge pixedl on the edge map obtained by applying the threshold selection scheme proposed in this paper. Achieving significantly improved results in terms of SNR as well as subjective quality, we can claim that the suggested method works well.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.6
• /
• pp.591-601
• /
• 2021

Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.