Research on credit card transaction security supervision based on PU learning by Renfeng CHEN, Hongbin ZHU

“…The complex and ever-evolving nature of credit card cash out methods and the emergence of various forms of fake transactions present challenges in obtaining accurate transaction information during customer interactions.In order to develop an accurate supervision method for detecting fake credit card transactions, a PU (positive-unlabeled learning) based security identification model for single credit card transactions was established.It was based on long-term transaction label data from cashed-up accounts in commercial banks’ credit card systems.A Spy mechanism was introduced into sample data annotation by selecting million positive samples of highly reliable cash-out transactions and 1.3 million samples of transactions to be labeled, and using a learner to predict the result distribution and label negative samples of non-cash-out transactions that were difficult to identify, resulting in 1.2 million relatively reliable negative sample labels.Based on these samples, 120 candidate variables were constructed, including credit card customer attributes, quota usage, and transaction preference characteristics.After importance screening of variables, nearly 50 candidate variables were selected.The XGBoost binary classification algorithm was used for model development and prediction.The results show that the proposed model achieve an identification accuracy of 94.20%, with a group stability index (PSI) of 0.10%, indicating that the single credit card transaction security identification model based on PU learning can effectively monitor fake transactions.This study improves the model discrimination performance of machine learning binary classification algorithm in scenarios where high-precision sample label data is difficult to obtain, providing a new method for transaction security monitoring in commercial bank credit card systems.…”
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Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center by Amir Hossein Hariri, Esmaeil Bagheri, Sayyed Mohammad Reza Davoodi

“…The present study aimed to determine the patterns of cardiovascular diseases using integrated classification techniques for analyzing the data of internal medicine patients who are at the risk of heart failure with 451 samples and 13 characteristics. …”
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Landslide Susceptibility Zonation Mapping Using Machine Learning Algorithms and Statistical Prediction at Hunza Watershed Basin, Pakistan by A. Khan, G. Khan, M. Minhas, S. A. Hussain Gardezi, J. Ahmed and N. Abbas

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QLearning based business differentiating routing mechanism in SDN architecture by Zijin JIN, Julong LAN, Yiming JIANG, Penghao SUN, Peng WEI

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The Exponential Diophantine Equation 2x+by=cz by Yahui Yu, Xiaoxue Li

“…In this paper, a classification of all positive integer solutions (x,y,z) of the equation 2x+by=cz is given. …”
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Anomaly domains detection algorithm based on historical data by Fu-xiang YUAN, Fen-lin LIU, Bin LU, Dao-fu GONG

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Android malware detection based on APK signature information feedback by Xin-yu LIU, Jian WENG, Yue ZHANG, Bing-wen FENG, Jia-si WENG

“…A new malware detection method based on APK signature of information feedback (SigFeedback) was proposed.Based on SVM classification algorithm,the method of eigenvalue extraction adoped heuristic rule learning to sig APK information verify screening,and it also implemented the heuristic feedback,from which achieved the purpose of more accurate detection of malicious software.SigFeedback detection algorithm enjoyed the advantage of the high detection rate and low false positive rate.Finally the experiment show that the SigFeedback algorithm has high efficiency,making the rate of false positive from 13% down to 3%.…”
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Deep belief network-based link quality prediction for wireless sensor network by Lin-lan LIU, Jiang-bo XU, Yue LI, Zhi-yong YANG

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Biology and Management of Spanish Needles (Bidens spp.) in Ornamental Crop Production by Yuvraj Khamare, Stephen Christoper Marble, Shawn T. Steed, Nathan S. Boyd

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Clinical Insight into Congenital Hypothyroidism Among Children by Hüseyin Anıl Korkmaz

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Divisão territorial do trabalho na indústria. A participação da mão de obra feminina by Cleverson Alexsander Reolon, Eliseu Savério Sposito

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Hypersurface Constrained Elasticae in Lorentzian Space Forms by Óscar J. Garay, Álvaro Pámpano, Changhwa Woo

“…We characterize critical geodesics showing that they live fully immersed in a totally geodesic M13(c) and that they must be of three different types. …”
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TRAINING AND PROFESSIONAL VOLUNTEER ORGANIZATIONS by V. A. STARODUBTSEV, P. V. RODIONOV

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Keystroke features recognition based on stable linear discriminant analysis by Wei-guo SHEN, Wei WANG

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THE TECHNOLOGY OF INFORMATION PROCESSING BY HIGHER EDUCATIONAL INSTITUTION STUDENTS: STAGES, METHODS, TECHNIQUES by Denis V. Tatyanushkin

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How to Start a Food Business: Basic Food Technology: Food Acidity by Soohyoun Ahn, Jayna Goldstein, George Baker, Matthew Krug

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ASSESSMENT OF POSSIBLE USE OF CARBON ISOTOPE RATIOS IN AMINO ACIDS FOR MEAT PRODUCT GEOGRAPHICAL ORIGIN IDENTIFICATION by A. I. Solovyev, I. V. Podkolzin

“…Upon results of isotope ratio (δ13C) determination in amino acids after gas chromatographic separation samples were classified using Support Vector Machines and linear discriminate analysis. …”
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Le sarcophage de pierre du haut Moyen Âge, des critères pour une étude by Fabrice Henrion

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Predictive models for the implementation of targeted reproductive management in multiparous cows on automatic milking systems by Fergus P. Hannon, Martin J. Green, Luke O'Grady, Chris Hudson, Anneke Gouw, Laura V. Randall

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Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning by M. Aqil, M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati, S. Panikkai, S. Suwardi, Z. Bunyamin, E. Roy, M. Muslimin, M. Yasin, E. Prakasa

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