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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM37514

SMART RESUME ANALYZER

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM37514

Volume:

8 |

9 |

Sep 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM37514

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

MAM

Mr. Ashish Modi

MHRC

Mr. Hassan Raza Chowdhary

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Abstract

In today’s competitive job market, the need for efficient and effective hiring processes is paramount. The Smart Resume Analyzer is an innovative AI-powered tool reimagining the recruitment landscape by seamlessly integrating advanced machine learning (ML) and natural language processing (NLP) techniques. Unlike traditional resume screening methods that are often hampered by manual evaluation, subjectivity, and inefficiencies, this system offers a cutting-edge approach to candidate assessment. It intelligently extracts and analyzes key information from resumes, utilizing sophisticated algorithms such as Cosine Similarity and Term Frequency-Inverse Document Frequency (TF-IDF) to provide real-time, data-driven evaluations. Moreover, this tool democratizes the job search process by empowering applicants with the knowledge and tools to refine their resumes, thereby leveling the playing field in a competitive job market. The Smart Resume Analyzer is more than just a recruitment aid—it is a transformative solution that bridges the gap between employers and the talent they seek, fostering a more efficient and equitable hiring process.

KEYWORDS

NLP (Natural Language Processing) Resume Parsing Machine Learning Recommender Systems Data Security Data Privacy User Engagement Semantic Analysis User Data Analysis: & Text Mining.

Cite This Article

Mr. Ashish Modi, Mr. Hassan Raza Chowdhary, "SMART RESUME ANALYZER," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 9, Sep 2024. https://doi.org/10.55041/IJSREM37514

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Publication Date

9/18/2024

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Double-Blind Peer Review

Review Rounds: 2

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0

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2

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5

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

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