Intelli360: An Intelligent Recruitment System Using Artificial Intelligence, Machine Learning, and Natural Language Processing

NAMANDEEP1, AMANDEEP2, DHARMENDER3, NARENDER4

M.Sc. Computer Science1, Artificial Intelligence and Data Science, GJUS&T Assistant Professor2, Artificial Intelligence and Data Science, GJUS&T Professor3, Artificial Intelligence and Data Science, GJUS&T

Assistant Professor4, cse department, GJUS&T

Abstract
Recruiting teams are increasingly confronted with the volume-versus-quality problem: the number of applications has grown faster than the ability of manual resume screening, and keyword-driven applicant tracking systems (ATS) routinely miss qualified candidates whose resumes don’t surface the exact terms a filter is looking for. In this paper, we propose a comprehensive end-to-end intelligent recruitment system, “Intelli360”, which exploits Natural Language Processing (NLP), supervised Machine Learning (ML) and predictive analytics to automate the process of resume parsing, skill extraction, ATS score prediction and candidate ranking. The system extracts structured information from unstructured resumes, computes a weighted resume-matching score against job descriptions and predicts an ATS suitability score. Recruiter and candidate dashboards, an AI chatbot and automated interview scheduling support the surrounding workflow. Intelli360 was benchmarked against a traditional keyword-based ATS and manual screening on a multi-domain resume set covering Artificial Intelligence, Data Science, Software Engineering, Web Development and Cybersecurity roles. Intelli360 achieved a matching accuracy of 91.8% compared to 82.3% for the keyword-based ATS and 72.5% for manual screening, a precision of 90.6%, recall of 92.9% and F1-score of 91.7%. Average resume screening time reduced from 18.4 seconds to 4.6 seconds per resume Average shortlisting time reduced from 6.5 days to 1.8 days Our results suggest that the combination of semantic, NLP-based skill extraction and ML-based ranking provides a significant improvement overrule-based and keyword-based recruitment pipelines. Further work is needed to statistically validate these gains (crossvalidation, confusion-matrix analysis, and significance testing).
Index Terms—recruitment automation, natural language processing, machine learning, resume parsing, ATS score prediction, candidate ranking, predictive hiring analytics.