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@article{181770,
        author = {Maya Chintaman Gulve and Sonali K. Shewale and Nagma Begum Lala Shah and Sanjyot Ganesh Tembulwar and Gautami Rajesh Chabukswar},
        title = {AI Resume Analyzer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5547-5551},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181770},
        abstract = {In the evolving landscape of recruitment, where organizations are inundated with thousands of resumes for a single job posting, efficient and intelligent resume screening has become a critical need. The AI Resume Analyzer project addresses this challenge by leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) to create a sophisticated and automated resume analysis tool. This system is designed to accurately parse, evaluate, and rank resumes based on customizable criteria such as job descriptions, skill requirements, experience levels, and educational qualifications.The core of AI Resume Analyzer lies in its hybrid architecture, which combines keyword extraction, semantic similarity scoring, and machine learning-based classification to assess candidate suitability. Using advanced NLP techniques, the system extracts relevant information from resumes—such as contact details, work history, technical and soft skills, and educational background—and maps them against job-specific criteria. A custom scoring algorithm is employed to rank applicants based on both quantitative metrics and contextual relevance, minimizing recruiter bias and ensuring objective evaluations.},
        keywords = {},
        month = {June},
        }