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@article{184866,
        author = {SHAIK OSIHA and D.MURALI},
        title = {PREDICTION OF FAKE JOB AD USING NLP-BASED MULTILAYER PERCEPTRON CLASSIFIER},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3204-3210},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184866},
        abstract = {In recent years, due to advancement in modern technology and social communication, advertising new job post has become very common issues in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posting prediction leaves a lot of challenges to face. The fraudulent post-detection work draws proper attention to obtaining an automated tool to identify fake jobs and report them to people to avoid applying for such situations. Human judgment can be subjective in some platforms. Most platforms rely on humans to flag suspicious job postings. Users can report ads and users report fake job or genuine. Moderators review them manually. Scammers exploit online job platforms with fake ads, wasting job seekers time and potentially harming them. Misidentifying legitimate ads or failing to detect certain types of scams. Manual review doesn't scale well with the vast number of online job postings. Project used Natural Language Processing (NLP) and a Multilayer Perceptron (MLP) classifier to predict if it's real or fake. Job search platforms like Indeed, Glassdoor and LinkedIn these are the applications. Valid job postings can be done by filtering out fake ad, using a Natural Language Processing technique in combination with TFIDF vectorization and Neural Network. With the help of the MLP job seekers find out their jobs depending on their qualification, experience, suitability etc. Recruitment process is now influenced social media.},
        keywords = {Fake Job Prediction, NLP, MLP, TFIDF, Neural Network, Machine learning.},
        month = {September},
        }