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@article{199937,
        author = {JANGETI LIKITHA and JAKKAMPUDI RAJITHA RANi and DUDALA SOWMYA SREE and Ms. Israelin Insulata. J},
        title = {FAKE JOB RECRUITMENT DETECTION USING MACHINE LEARNING APPROACH},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1324-1329},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199937},
        abstract = {To avoid fraudulent post for job in the internet, an automated tool using machine learning based classification techniques is proposed in the paper. DU/érent classifiers are used [or checking fraudulent post in the web and the results of those class(/iers are compared for identifj'ing the best employment scam detection model. It helps in detecting fake job posts from an enormous number of posts. Two major types of classifiers, such as single class(/ier and ensemble classifiers are considered for fraudulent job posts detection. However, experimental results indicate that ensemble classifiers are the best classification to detect scams over the single classifiers.
Experimental results demonstrate that ensemble models, particularly Random Forest and XGBoost, achieve over 95% accuracy in distinguishing fake from legitimate postings.},
        keywords = {Fake Job: Online Recruitment, Machine Learning, Ensemble Approach.},
        month = {May},
        }