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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | FAKE JOB RECRUITMENTS DETECTION USING MACHINE LEARNING |
| Authors Name: | MANIDEEP MOTURI , RAJ HEMANI , MANISH KUMAR |
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| Author Reg. ID: |
IJRTI_182655
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| Published Paper Id: | IJRTI2206181 |
| Published In: | Volume 7 Issue 6, June-2022 |
| DOI: | |
| Abstract: | To avoid fraudulent post for job in the internet, an automated tool using machine learning based classification techniques is proposed in the paper. Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best employment scam detection model. It helps in detecting fake job posts from an enormous number of posts. Two major types of classifiers, such as single classifier and ensemble classifiers are considered for fraudulent job posts detection. However, experimental results indicate that ensemble classifiers are the best classification to detect scams over the single classifiers. The critical process of hiring has relatively recently been ported to the cloud. Specifically, the automated systems responsible for completing the recruitment of new employees in an online fashion, aim to make the hiring process more immediate, accurate and cost-efficient. However, the online exposure of such traditional business procedures has introduced new points of failure that may lead to privacy loss for applicants and harm the reputation of organizations. So far, the most common case of Online Recruitment Frauds (ORF), is employment scam. Unlike relevant online fraud problems, the tackling of ORF has not yet received the proper attention, remaining largely unexplored until now. Responding to this need, the work at hand defines and describes the characteristics of this severe and timely novel cyber security research topic. At the same time, it contributes and evaluates the first to our knowledge publicly available dataset of 17,880 annotated job ads, retrieved from the use of a real-life system. |
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| Cite Article: | "FAKE JOB RECRUITMENTS DETECTION USING MACHINE LEARNING", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 6, page no.1147 - 1185, June-2022, Available :http://www.ijrti.org/papers/IJRTI2206181.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2206181
Registration ID:182655
Published In: Volume 7 Issue 6, June-2022
DOI (Digital Object Identifier):
Page No: 1147 - 1185 Country: RAJANNA SIRCILLA, TELANGANA, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2206181 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2206181 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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