
Journal of FST

Journal of FST

Bangladesh University of Professionals (BUP)
Publishing Model
Hybrid
Electronic ISSN
3134-7339
Print ISSN
2959-4812
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Research Article
Author(s): Md Istakiak Adnan Palash, Arijit Diganto, Osama Nazmul Fatan, Kazi Abu Taher, Md Jaber Al Nahian
Article Info: Journal of FST, ISSN: 2959-4812, Volume - 01, Issue - 01, July 2022, Article #10
Publish Date: July 1, 2022
Author(s): Md Istakiak Adnan Palash, Arijit Diganto, Osama Nazmul Fatan, Kazi Abu Taher, Md Jaber Al Nahian
DOI: -
Keywords: Fake Job Posting, COVID-19, Detection, Machine Learning, CNN
User Activity: Views: 2002, Downloads: 1997, Citations: 0
The present era focuses on every aspect of modern civilization that can be handled online, such as internet banking, teaching, safety, and employment, etc. This advancement in technology makes it easy for scammers to make money very quickly by looting people. Fake job advertisements are among the latest scams. When people apply for these fake jobs, they have topay fees and send their personal information to the fraudsters, which results in a scam and losing money. Therefore, in this paper, we have proposed a novel Convolutional Neural Network(CNN) to identify fake job postings efficiently. A publicly available dataset named EMSCAD was used to validate our proposed model. A comparison was also made between our proposed model and several state-of-the-art machine learning algorithms. In our experiments, we found that our proposed model had a greater accuracy than other machine learning algorithms. In addition, this study conducts a critical comparison of our method with the most recent existing studies.
Md Istakiak Adnan Palash, Arijit Diganto, Osama Nazmul Fatan, Kazi Abu Taher, Md Jaber Al Nahian. (July 1, 2022). Detection of Fake Job Postings on Online Using Convolutional Neural Network. Journal of FST, Volume 01, Issue 01.