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@article{179513,
        author = {Rohini M and Snega T and Chinthamani V and Sowmiya Shree. N.L and Dharani K},
        title = {Ai-Powered Resume Parser for Automated Candidate Screening Using Automated Classification Logistic Regression System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6862-6868},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179513},
        abstract = {Recently, the limited job opportunities and the primary reasons for updating resumes have become major contributors to the issue of unemployment. Early updates and continuous monitoring of resume automation using Machine Learning (ML) can be costly. To address the challenges associated with the Automated Classification Logistic Regression System Classification Method, early updates and automation of job applications improve the accuracy of results, and the collection of documents becomes more secure. Moreover, the Text Preprocessing Module eliminates duplicate data, minimizes unknown data, and maximizes valuable data during preprocessing. The Term Frequency-Inverse Document Frequency (TF-IDF) separates each data value type, starting with the minimum representation to reduce the unpredictable values and enhance resume update performance. Ultimately, the proposed method classifies data, tests it, and validates the predicted outcomes to assess performance. Calculation is a training process, while the testing data is multi-level in classification. Each type of data connects to its network within the classification framework. It facilitates comprehensive monitoring based on input data from video tests of the training data and checks the various recruitment models involved in the process. Finally, it is automatically updated to assist in the job application process. The proposed method offers greater reliability and achieves high performance while maintaining standard scalability. These techniques reduce time complexity, ensuring performance remains within an accurate range of 91%.},
        keywords = {Automated Classification Logistic Regression System, Term Frequency-Inverse Document Frequency (TF-IDF), Text Preprocessing Module, Automatic Classification.},
        month = {May},
        }