• International journal of advanced smart convergence
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• 제11권4호
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• pp.47-56
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• 2022

Recently bank card number recognition plays an important role in improving payment efficiency. In this paper we propose a new bank-card number identification algorithm. The proposed algorithm consists of three modules which include edge detection, candidate region generation, and recognition. The module of 'edge detection' is used to obtain the possible digital region. The module of 'candidate region generation' has the role to expand the length of the digital region to obtain the candidate card number regions, i.e. to obtain the final bank card number location. And the module of 'recognition' has Convolutional deep learning Neural Network (CNN) to identify the final bank card numbers. Experimental results show that the identification rate of the proposed algorithm is 95% for the card numbers, which shows 20% better than that of conventional algorithm or method.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.112-115
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• 2020

Trump cards are used in variable games in households such as poker and blackjack. In many cases, it is able to be helpful to algorithmically identify the playing cards from camera views. In this paper, we provide an approach that detects and identifies the playing card using template matching scheme, and evaluate the results of the provided implementation. For ideal cases, the implemented system provides a 100% success rate for card identification correct. However, non-ideal case of perspective distortion is estimated with 70% success ratio. This work aims to evaluate the effectiveness of augmented reality user interface for an entertainment application like playing card games.

• 한국지능시스템학회논문지
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• 제11권5호
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• pp.400-405
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• 2001

This paper addresses an effective approach of training neural networks classifiers for credit card fraud detection. The proposed approach uses evolutionary programming to trails the neural networks classifiers based on maximization of the detection rate of fraudulent usages on some ranges of the rejection rate, loot minimization of mean square error(MSE) that Is a common criterion for neural networks learning. This approach enables us to get classifier of satisfactory performance and to offer a directive method of handling various conditions and performance measures that are required for real fraud detection applications in the classifier training step. The experimental results on "real"credit card transaction data indicate that the proposed classifiers produces classifiers of high quality in terms of a relative profit as well as detection rate and efficiency.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• 한국항공운항학회지
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• 제19권4호
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• pp.18-23
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• 2011

Generally, the digital circuit card can be tested by automatic test equipment using LASAR(Logic Automated Stimulus and Response). This paper proposes the ROM data extracting algorithm which can test the digital circuit card that consists usually ROMs. We are implemented of the proposed fault detecting program by LabWindow/CVI 8.5 and the digital automatic test instrument with NI-VXI(National Instrument - Versa Bus Modular Europe eXtentions for Instrumentation) card. We also make an interface circuit board connecting the digital test instrument and the digital circuit card. It shows the good performance of getting the data from ROMs.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.68-98
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• 2004

Although the internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders because the chance of detection and punishment are decreased. One of fraud is phantom transaction which is a colluding transaction by the buyer and seller to commit illegal discounting of credit card. They pretend to fulfill the transaction paid by credit card, without actual selling products, and the seller receives cash from credit card corporations. Then seller lends it out buyer with quite high interest rate whose credit score is so bad that he cannot borrow money from anywhere. The purpose of this study is to empirically investigate the factors to detect of the phantom transaction in online auction. Based up on the studies that explored behaviors of buyers and sellers in online auction, bidding numbers, bid increments, sellers' credit, auction length, and starting bids were suggested as independent variables. We developed an Internet-based data collection software agent and collect data on transactions of notebook computers each of which winning bid was over 1,000,000 won. Data analysis with logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transaction.

• 경영과학
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• 제21권2호
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• pp.273-289
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• 2004

Although the Internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders, because the chance of detection and punishment is decreased. One of these frauds is the phantom transaction, which is a colluding transaction by the buyer and seller to commit the illegal discounting of a credit card. They pretend to fulfill the transaction paid by credit card, without actually selling products, and the seller receives cash from the credit card corporations. Then the seller lends it out with quite a high interest rate to the buyer, whose credit rating is so poor that he cannot borrow money from anywhere else. The purpose of this study is to empirically investigate the factors necessary to detect phantom transactions in an online auction. Based upon studies that have explored the behaviors of buyers and sellers in online auctions, the following have been suggested as independent variables: bidding numbers, bid increments, sellers' credit, auction lengths, and starting bids. In this study. we developed Internet-based data collection software and collected data on transactions of notebook computers, each of which had a winning bid of over W one million. Data analysis with a logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transactions.

• 한국경제지리학회지
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• 제15권4호
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• pp.615-627
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• 2012

본 연구는 교통카드 데이터를 이용하여 대중교통 이용과 관련하여 통행패턴을 분석하였으며 교통정책에 활용할 수 있는 방안을 제시하였다. 교통카드 데이터는 경기도권역을 대상으로 하였고 활용방안으로 교통정책 의사결정자가 버스정류소 시설을 개선할 때 교통카드데이터에서 얻어질 수 있는 여러 변수를 이용하여 대상지를 선정한다는 시나리오를 설정하여 분석하였다. 분석결과, 의사결정방법론인 K평균 군집분석과 CHAID(Chi-squared automatic interaction detection)를 이용하였으며, 유의수준 p<0.01에서 정책에 유용하게 이용될 수 있는 결과를 얻었다. 또한 본 연구에서는 이러한 결과들을 근거로 교통카드데이터를 실제로 정책에 활용되기 위해서 개선되어야 할 정책적 함의를 제시하였다.

• 정보보호학회논문지
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• 제25권6호
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• pp.1525-1540
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• 2015

금융 산업과 IT 기술의 결합으로 지불 방법이 간편화됨에 따라 소비자의 지불 수단이 현금 결제에서 신용카드, 모바일 소액결제, 앱카드 등을 이용한 전자결제로 변화되고 있다. 이에 전자금융결제를 악용하여 이상거래를 시도하는 사례가 증가하는 추세로, 금융사는 이상거래로부터 소비자를 보호하기 위해 FDS(Fraud Detection System)를 구축하고 있다. 이상거래 탐지 시스템은 실시간으로 이용자 정보와 결제 정보를 분석하여 높은 정확도로 이상거래를 식별하는 것이 목표이다. 본 연구에서는 결제로그 분석 및 데이터 마이닝을 이용한 이상거래 탐지 연구 동향을 조사하였으며, 이상거래 탐지에 사용된 데이터 마이닝 알고리즘을 정리하고 이상거래 탐지 연구를 사용된 데이터 셋, 알고리즘, 연구 관점으로 분류하였다.

• 한국ITS학회 논문지
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• 제17권6호
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• pp.40-53
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• 2018

교통카드자료에서 철도이용승객이동은 단말기ID 순서로 나타난다. 최초 단말기ID는 진입역사 Tag-In노선, 최종 단말기ID는 진출역사 Tag-Out노선, 중간 단말기ID는 환승역사 Tag노선정보를 각각 포함한다. 과거 3개 공사기관(서울교통공사, 인천교통공사, 한국철도공사)만 참여하던 수도권도시철도는 최초 및 최종 단말기ID로 표현된 OD만 존재했다. 최근 (주)신분당선, (주)우이-신설경전철 등 민자기관진입으로 진입환승노선 단말기ID와 진출환승노선 단말기ID가 포함된 Chain OD가 보편화되었다. Chain OD를 통한 정확한 경로구축과정은 수도권철도운송기관의 수입금배분의 기초자료로서 매우 중요한 위치를 차지하고 있다. 따라서 지하철네트워크에서 경로탐색은 Chain OD에 대한 최적경로탐색의 문제로 전환되어 새로운 해법이 요구된다. 본 연구는 철도네트워크에서 단말기 Chain OD의 최초 단말기ID와 최종 단말기ID 간의 최적경로탐색기법을 제안하는 것이다. 이때 Chain OD에 민자노선환승 TagIn/Out를 반영하여 최적경로를 탐색하는 방안을 강구한다. 이를 위해 링크표지로 구축된 3가지 경로탐색기법( 1) 노드 - 링크, 2) 링크 -링크, 3) 링크 -노드 )을 순차적으로 적용하는 방안을 제안한다. 가산성경로비용을 토대로 제안된 기법이 최적조건을 만족함을 증명한다.