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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM36789

Money Laundering Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM36789

Volume:

8 |

7 |

Jul 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SKP

Shankar K P

VS

Vidya S

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Abstract

Managing large amounts of data can be challenging in this era. Information Extraction and Ontology Learning are two vital technologies that address this difficulty. IE automatically retrieves and utilizes required data from unstructured data, using Named Entity Recognition techniques. OL is used to construct a formal representation of knowledge by organizing data into concepts and relationships between them based on the specific domain. These technologies enhance data organization, searchability, and interoperability, and when combined, they enable effective data analysis, tailored services, and the development of intelligent systems. They also play a vital role in advancing e-learning, as they retrieve and structure information related to the education sector, providing more organized and personalized efficient details for students and users. By leveraging IE and OL, educators can better align learning materials with student needs and track educational trends, improving the overall learning experience.

KEYWORDS

Anti-Money Laundering (AML) Machine Learning (ML) Financial Fraud Detection Random ForestReal-Time Prediction Flask Data Preprocessing

Cite This Article

Shankar K P, Vidya S, "Money Laundering Detection," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 7, Jul 2024.

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Publication Date

7/24/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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