An Intelligent Arabic Model for Recruitment Fraud Detection Using Machine Learning

Abstract

Over the last years, with the tremendous growth of digital transformation and the constant need for companies to hire employees, huge amounts of fraudulent jobs have been posted on the internet. A cleverly planned sort of scam aimed at job searchers for a variety of unprofessional purposes is a false job posting. It can lead to a loss of money and effort. An Arabic intelligent model has been built to avoid fraudulent jobs on the Internet using machine learning, data mining, and classification techniques. The proposed model is applied to the Arabic version of the EMSCAD dataset. It is available on the Internet in the English version and it has been retrieved from the use of a real-life system and consists of several features such as company profile, company logo, interview questions, and more features depending on job offer ads, Firstly, EMSCAD is translated into the Arabic language. Then, a set of different classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) was used to detect the fraudulent jobs. Finally, the results were compared to determine the best classifier used for detecting fraudulent jobs. The proposed model achieved better results when using a Random Forest classifier with 97% accuracy.

Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

“…Moreover, an innovative learning model is the critical core technology of intelligent learning, and constructing an innovative learning model has become an important task and challenge in innovative learning [2]. Currently, the application of information technology in the field of education only as an auxiliary tool, only in the original teaching mode, improves teaching efficiency but does not manage to do the deep integration of information technology and education, did not significantly change to improve the quality of classroom teaching [3]. As the essential part of intelligent learning, constructing an intelligent learning model is conducive to improving the quality of intelligent new teaching modes, thus leading to the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching under the support of artificial intelligence.…”