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عنوان
Reduce fraud risk Using CNN and LSTM and Time Series Data in E-Commerce

پدید آورنده
Hayder Al Jassim,Al Jassim,

موضوع
Internet of Things (IoT), banks, fraud monitoring, (CNN), deep learning.,اینترنت اشیا (IoT)، بانک ها، نظارت بر تقلب، (CNN)، یادگیری عمیق.

رده

کتابخانه
University of Tabriz Library, Documentation and Publication Center

محل استقرار
استان: East Azarbaijan ـ شهر: Tabriz

University of Tabriz Library, Documentation and Publication Center

تماس با کتابخانه : 04133294120-04133294118

NATIONAL BIBLIOGRAPHY NUMBER

Number
T27746

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Reduce fraud risk Using CNN and LSTM and Time Series Data in E-Commerce
General Material Designation
Dissertation
First Statement of Responsibility
Hayder Al Jassim

.PUBLICATION, DISTRIBUTION, ETC

Name of Publisher, Distributor, etc.
Electrical and Computer Engineering
Date of Publication, Distribution, etc.
1401

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
87p.
Other Physical Details
cd

DISSERTATION (THESIS) NOTE

Dissertation or thesis details and type of degree
M.S.
Discipline of degree
Software Engineering Field of Data Mining
Date of degree
1401/07/19

SUMMARY OR ABSTRACT

Text of Note
Fraud and forgery in bank cards or financial transactions is the illegal activity of a person or people in a bank account that does not belong to them. If a person illegally uses another person's bank account, he has committed credit card fraud. Today, due to the growth of the Internet of Things and virtual banking, such frauds have increased significantly. Artificial intelligence and data mining should be used to identify these frauds with high accuracy. In this thesis, using deep learning, convolutional neural networks, and LSTM, we try to identify fraud in bank transactions with high accuracy. CNNs have a good ability to find spatial features, and in contrast, LSTM can find temporal relationships between data well. The combination of these two deep networks can lead to a very accurate bank card fraud detection system.

OTHER VARIANT TITLES

Variant Title
کاهش خطر تقلب با استفاده از CNN و LSTM و داده های سری زمانی در تجارت الکترونیک

UNCONTROLLED SUBJECT TERMS

Subject Term
Internet of Things (IoT), banks, fraud monitoring, (CNN), deep learning.
Subject Term
اینترنت اشیا (IoT)، بانک ها، نظارت بر تقلب، (CNN)، یادگیری عمیق.

PERSONAL NAME - PRIMARY RESPONSIBILITY

Entry Element
Al Jassim,
Part of Name Other than Entry Element
Hayder
Relator Code
Producer

PERSONAL NAME - SECONDARY RESPONSIBILITY

Entry Element
Balafar,
Entry Element
Salehpoor,
Part of Name Other than Entry Element
Mohammad Ali
Part of Name Other than Entry Element
Pedaram
Relator Code
Thesis advisor
Relator Code
Consulting advisor

CORPORATE BODY NAME - SECONDARY RESPONSIBILITY

Entry Element
Tabriz

ORIGINATING SOURCE

Country
ایران
Agency
Central Library of Tabriz University

LOCATION AND CALL NUMBER

Call Number
ارشد پایاننامه QA76.J37 1401

ELECTRONIC LOCATION AND ACCESS

Electronic name
Hayder Al Jassim
Contact for access assistance
عبادی

e

TL
276903

a
Y

Proposal/Bug Report

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