International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Automated Candidate Shortlisting System Based on AI |
|---|---|
| ABSTRACT | The rapid increase in job applications has made traditional resume screening processes inefficient, time- consuming, and prone to human bias. Recruiters often face difficulty in manually analyzing thousands of resumes, leading to delays and inconsistent hiring decisions. To overcome these challenges, this project presents an AI-based Automated Candidate Shortlisting System that assists recruiters by intelligently analyzing, classifying, and ranking resumes using Natural Language Processing (NLP) and Machine Learning (ML) techniques. The proposed system includes an automated resume parsing module that extracts key candidate information such as personal details, skills, education, work experience, projects, certifications, and languages from resumes. The system supports multi-language resumes, enabling accurate processing of documents written in English, Hindi, and other supported languages. An AI-driven skill matching mechanism compares candidate profiles with job requirements to generate a match score and identify missing skills. To promote fair hiring practices, the system incorporates bias analysis by minimizing the influence of irrelevant attributes during evaluation. Based on experience analysis, candidates are automatically classified as Fresher, Mid-level, or Senior. The system further automates recruitment workflows by shortlisting candidates into Selected, Rejected, or Review Required categories. Overall, this project demonstrates how artificial intelligence can enhance recruitment efficiency, reduce bias, and enable data-driven hiring decisions in real-world recruitment processes |
| AUTHOR | PROF. VAISHNAVI G SONAWANE, SAMRUDDHI PRAVIN BANDAL, SHATAKSHI AMOL BHAGWAT, YASHASVI DHIRAJ CHAVAN, KASTURI RATNAKAR KANK Guide, Department of Artificial Intelligence & Machine Learning, AISSMS's Polytechnic, Pune, Maharashtra, India Department of Artificial Intelligence & Machine Learning, AISSMS's Polytechnic, Pune, Maharashtra, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404007 |
| pdf/7_Automated Candidate Shortlisting System Based on AI.pdf | |
| KEYWORDS | |
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