FAKE JOB POST PREDICTION

FAKE JOB POST PREDICTION
Author(s):
kambam bindu, Dr. K Santhi Sree
Keywords:
- Job fraud detection, classification, DNN, KNN, SVM, Naive Bayes, Decision Tree, Random Forest, data mining, EMSCAD dataset.
Abstract
Because of the improvement of web-based entertainment and contemporary advancements, posting job opportunities has turned into a boundless issue in the present society. The issue of fake job posting prediction will consequently be of significant interest to everybody. Predicting fake job postings, like many other contractual issues, poses many challenges. To determine whether a job advertisement is real or fake, the company is expected to use a variety of data mining techniques and classification algorithms, including ANN, decision trees, support vector machines, naive Bayes classifiers, random forest classifiers, multi-layer perceptron, and deep neural networks. We conducted our tests using the Employment Scam Aegean Dataset (EMSCAD), which contains 18,000 examples. The exhibition of the deep neural network classifier is magnificent for this order test. For this DNN classifier, three thick layers were utilized. As far as predicting a fake job post, the prepared classifier has a classification accuracy (DNN) of practically 98%.
Article Details
Unique Paper ID: 166900 Publication Volume & Issue: Volume 11, Issue 2 Page(s): 2203 - 2210
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