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http://10.1.7.192:80/jspui/handle/123456789/11900
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DC Field | Value | Language |
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dc.contributor.author | Raval, Jay | - |
dc.date.accessioned | 2023-08-18T08:59:18Z | - |
dc.date.available | 2023-08-18T08:59:18Z | - |
dc.date.issued | 2023-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/11900 | - |
dc.description.abstract | In this paper, we present an artificial intelligence(AI) and blockchain-based credit card fraud detection system for detecting fraud transactions in the dataset. This dataset has 284808 rows P with 31 columns Q and 0.17 % fraud class data. In the data preprocessing step, clean the data and normalized the feature. For a select important feature, we use explainable artificial intelligence(XAI) to get the highest priority feature in the dataset. Long short-term memory (LSTM) is used to detect fraud in the system and gives better accuracy. Blockchain is a decentralized system to secure the transaction of the system using smart contracts and an InterPlanetary File System(IPFS). After all processes, the LSTM gives 99.8% accuracy with using XAI. Also, present the comparison between two LSTM results with and without using XAI. Then we save the non-fraud transaction data using smart contracts and blockchain. Finally, we conclude our proposed system architecture with the results. Keywords: Explainable artificial intelligence, credit card frauds, deep learning, long short-term memory, fraud classification | en_US |
dc.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 21MCED18; | - |
dc.subject | Computer 2021 | en_US |
dc.subject | Project Report 2021 | en_US |
dc.subject | Computer Project Report | en_US |
dc.subject | Project Report | en_US |
dc.subject | 21MCE | en_US |
dc.subject | 21MCED | en_US |
dc.subject | 21MCED18 | en_US |
dc.subject | CE (DS) | en_US |
dc.subject | DS 2021 | en_US |
dc.title | Artificial Intelligence and Blockchain-based Financial Fraud Detection | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, CE (DS) |
Files in This Item:
File | Description | Size | Format | |
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21MCED18.pdf | 21MCED18 | 1.8 MB | Adobe PDF | ![]() View/Open |
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