World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , Scope Database , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

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    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • New Issue Published

    Its Our pleasure to inform you that, WJERT March 2023 Issue has been Published, Kindly check it on

  • WJERT New Impact Factor

    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from  5.549 to 5.924 due to high quality Publication at International Level


    MARCH 2023 Issue has been successfully launched on 1 March 2023.




Dr. S. Anitha* and Dr. N. Sridevi


Use of credit card urges people to purchase items online through the Internet. People will in general do a lot of buying on the web or in person by using the credit card. Charge cards have ended up being the most conspicuous office accessible to the individuals around the world to energize paperless exchanges at a colossal speed. At whatever point any such exchange occurs in trades or net advertising by utilizing a paperless structure, it is oppressed under high danger of fake exchanges because of numerous traps in the security arrangement of the utilization of credit cards on the systems. In this paper, supervised learning algorithms namely Logistic Regression, Decision Tree and Random Forest algorithms are involved to discover if the credit card transactions are fake or not. Performance evaluation metrics like precision, recall, F1-score and Matthews Correlation Coefficient are used to measure the performance of the algorithms. After the experimental outcomes, it is witnessed, the Random Forest algorithm outperforms better when compared to the other two algorithms.

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