Open AccessEnsemble Modeling on Job Scam Detection
Author(s) -
Raj Shree,
D. Egfin Nirmala,
S Sweatha,
Sweta Sneha
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1916/1/012167
Subject(s) - computer science , task (project management) , perspective (graphical) , audit , work (physics) , computation , fuse (electrical) , information sensitivity , data science , world wide web , internet privacy , computer security , artificial intelligence , business , mechanical engineering , electrical engineering , accounting , management , algorithm , engineering , economics
The new advancement of online enrollment and occupation enlistment systems has made another media for web fraudsters. The improvement of the update of ongoing innovation has restricted a few organizations to move into electronic board structures. The benefits of such electronic edges are liberal. From one perspective, they are the most ideal approach to lead numerous organizations’ applicants, and afterward once more, the contenders while following a task will be more involved. Community with bogus motives research these structures for sensitive information to be optimized by techniques for fake counteractive action advancements. In this article we are utilizing open informational indexes and AI computations to arrange phony or real posts. The target of this audit is to fuse both content, put together information and meta-information with respect to the situations in the work. The informational collection can be utilized to make characterization models that can acclimate themselves with the deceptive arrangements of duties. Gathering information models utilizing request computations like Logistic Regression, K Nearest Neighbors and RandomForest.