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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60480

AI Resume Analyzer

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM60480

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AS

Ashraf Sheikh

Rs

Rehmatulla sheikh

SM

Swayam Mohardiker

SK

Swayam Kesharvani

OM

Ozair Madni

VA

Vishesh Awale

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Abstract

In the contemporary recruitment landscape, the massive volume of applications makes manual resume screening time-consuming, prone to human bias, and often inefficient. This review paper examines the evolution and current state of AI-powered resume analysis systems that leverage Natural Language Processing (NLP) and Machine Learning (ML) to automate candidate evaluation. We analyze various methodologies, ranging from traditional

KEYWORDS

based filtering to advanced semantic analysis using Large Language Models (LLMs). The paper evaluates how these systems extract key attributes—such as skills education and experience—to calculate Applicant Tracking System (ATS) compatibility scores and match candidates to specific job descriptions. Furthermore we discuss the role of these tools in promoting fairness by reducing unconscious bias while addressing critical challenges like data privacy and the “semantic gap” in automated parsing. This review concludes by highlighting future directions including the integration of explainable AI and real-time job recommendation engines to further streamline the hiring process

Cite This Article

Ashraf Sheikh, Rehmatulla sheikh, Swayam Mohardiker, Swayam Kesharvani, Ozair Madni, Vishesh Awale, "AI Resume Analyzer," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026.

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Publication Date

4/17/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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