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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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09 May 2023, Volume 38 Issue 3
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Abstract
Suspicious transaction identification is an important element of anti-money laundering work, and it has become a new trend to use algorithmic models as tools to analyze and identify suspicious transactions. Deep convolutional neural networks can effectively and automatically extract classification features from data, and have been widely used in various fields of research as they have shown good recognition results in many classification tasks. In this paper, we firstly design an 8-layer model framework based on Alexnet network theory for suspicious transaction identification analysis. Secondly, the Elliptic data set is divided into training and testing sets in the ratio of 7:3, and the divided data are used to train and test the model. Finally, the robustness of the model to data input is explored by randomly disrupting the ordering of the elements in the input data. The results show that the Alexnet convolutional neural network has good applicability for suspicious transaction recognition, and the overall classification accuracy of the Elliptic dataset can reach 94%, and the proposed model in this paper has good recognition accuracy in general.
Keyword
Alexnet, part defects, deep learning, board parts
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