Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM50445
Volume:
9 |
6 |
Jun 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
â The research suggests using machine learning classification methods to identify and prevent fake job postings online. Nowadays, many companies prefer to advertise their job vacancies on the internet for easy access by job seekers. However, there are scammers who trick people into paying for non-existent jobs. Through data analysis and machine learning, we can distinguish between legitimate and fake job postings. Various algorithms are utilized to detect fraudulent posts and protect job seekers from falling victim to scams. The system is designed to teach the model how to distinguish between real and fake job listings using past data on fraudulent and legitimate job postings. Initially, supervised learning algorithms like classification techniques can be used to tackle the problem of identifying scammers in job ads. It will utilize multiple machine learning algorithms and choose the one with the best accuracy in predicting if a job posting is legitimate or not.
KEYWORDS
Publication Date
6/16/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
0
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...