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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

AI POWERED RESUME SCREENING SYSTEM USING NLP AND ML

VIJAYALAKSHMI P SRINANTHITHA L SABISRI S

PRADEEPA K

Department of Computer Science and Engineering E.G.S.Pillay  Engineering College Nagapattinam Tamilnadu India
DOI:https://doi.org/10.55041/ijcope.v2i4.799

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Abstract

The rapid increase in job applications has made manual resume screening inefficient and time-consuming. This paper proposes an AI-powered resume screening system using Natural Language Processing (NLP) and Machine Learning (ML) techniques to automate candidate evaluation. The system extracts relevant information such as skills, education, and experience from resumes and ranks candidates based on job requirements. NLP techniques are used for text processing, while ML models are applied for classification and ranking. The proposed system improves recruitment efficiency, reduces bias, and enhances decision-making accuracy.

Keypoints — Artificial Intelligence, Resume Screening, Natural Language Processing, Machine Learning, Recruitment Automation

How to Cite this Paper

P, V., L, S. & S, S. (2026). AI Powered Resume Screening System using NLP and ML. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.799

P, VIJAYALAKSHMI, et al.. "AI Powered Resume Screening System using NLP and ML." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.799.

P, VIJAYALAKSHMI,SRINANTHITHA L, and SABISRI S. "AI Powered Resume Screening System using NLP and ML." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.799.

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References

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[2] T. Mikolov et al., “Efficient Estimation of Word Representations in Vector Space,” 2013. https://arxiv.org/abs/1301.3781

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[7] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” 2011.https://jmlr.org/papers/v12/pedregosa11a.html

 

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  • •Published on: Apr 27 2026
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