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Journal of Artificial Intelligence and Capsule Networks

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Home / Archives / Volume-4 / Issue-3 / Article-6

Volume - 4 | Issue - 3 | september 2022

Detection of Fake Job Advertisements using Machine Learning algorithms
E. Baraneetharan   135  69
Pages: 200-210
Cite this article
Baraneetharan, E. (2022). Detection of Fake Job Advertisements using Machine Learning algorithms. Journal of Artificial Intelligence and Capsule Networks, 4(3), 200-210. doi:10.36548/jaicn.2022.3.006
Published
14 October, 2022
Abstract

Most companies nowadays use digital platforms to host conferences, job interviews, and other business events. The unexpected increase in the need for internet platforms has resulted in a rapid rise of fraud advertising. The agencies as well as fraudsters recruit the job seekers using a variety of techniques, including sources from online job-providing websites. By applying Machine Learning algorithms, researchers aim to decrease the number of such fraudulent and fake attempts. In this article, classifiers such as K-Nearest Neighbour, Support Vector Machine, and Extreme Gradient Boosting algorithms are implemented for fake advertisement prediction. The performances of the machine learning algorithms are evaluated using metrics such as accuracy, F1 measures, precision and recall.

Keywords

Job interviews fraudulent advertisements machine learning algorithm KNN SVM XGboost performance metrics

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