Detection of Fake Online Recruitment Using Machine Learning
P. Ashok Kumar
, G. Likitha , B. Nithin , A. Akash , G. Saikumar
Adaptive Sympathy, Synthetic Minority Class Oversampling, Term Frequency-Inverse Document Frequency, Feature Extraction.
The proliferation of online job platforms has introduced new challenges, particularly the surge in deceptive practices such as fake job postings. This research proposes a comprehensive solution leveraging advanced machine learning techniques to identify and mitigate the risks associated with fraudulent online recruitment. The system is designed to enhance the security and trustworthiness of the digital job-seeking landscape. Key features extracted from job descriptions, qualifications, and company details form the foundation for training robust machine learning models. Several algorithms, including support vector machines, random forests, and deep neural networks, are explored to discern patterns indicative of deceptive recruitment practices. Natural language processing techniques are integrated to imbue the system with semantic understanding, enabling nuanced analysis of job descriptions for subtle cues associated with fraudulent intent. Upon validation, the system is deployed on online job platforms, actively detecting and filtering out deceptive job postings in real-time. In summary, the proposed intelligent system addresses the critical need for an effective and adaptive solution to counter fake online recruitment. By integrating advanced machine learning and NLP techniques, the system contributes to the creation of a secure and trustworthy environment for both job seekers and online job platforms.
"Detection of Fake Online Recruitment Using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 5, page no.1064 - 1074, May-2024, Available :https://ijsdr.org/papers/IJSDR2405142.pdf
Volume 9
Issue 5,
May-2024
Pages : 1064 - 1074
Paper Reg. ID: IJSDR_211442
Published Paper Id: IJSDR2405142
Downloads: 000347383
Research Area: Computer Science & Technology
Country: Medchal, Telangana, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
