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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65349

AI-BASED RESUME ATS SCORE ANALYZER

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65349

Volume:

10 |

6 |

Jun 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65349

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

PSG

Prasad Shriram Ghodake

Department of Artificial Intelligence and Data Science

Sahyadri Valley College Of Engineering and Technology

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ADG

Abhishek Dilip Gawate

Department of Artificial Intelligence and Data Science

Sahyadri Valley College Of Engineering and Technology

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VJW

Vaibhav Jivan Wadagale

Department of Artificial Intelligence and Data Science

Sahyadri Valley College Of Engineering and Technology

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Mentors

PRD

Prof. Rahane D.A.

Department of Artificial Intelligence and Data Science

Sahyadri Valley College Of Engineering and Technology

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Abstract

The recruitment industry is using Applicant Tracking Systems (ATS) more and more to automatically screen resumes and select candidates. But many good candidates are not getting through the ATS filters because their resumes are not formatted correctly they are missing keywords and they do not match the job descriptions well. This paper talks about an AI-Based Resume ATS Score Analyzer. It uses Artificial Intelligence (AI) Natural Language Processing (NLP) and Machine Learning to evaluate resumes. The system looks at the resume content.It compares it with the job descriptions.It finds matching keywords.It analyzes how similar the resume and job description are in meaning.It gives an ATS score that shows how suitable the candidate is for the job.The system also gives advice on how to make the resume better and increase the chances of getting selected for the job.Tests show that the system is better at matching resumes with jobs. It also reduces the work recruiters have to do and makes the recruitment process more efficient.The solution helps both people looking for jobs and recruiters. It offers a easy-to-use and scalable platform, for evaluating resumes.

KEYWORDS

Artificial Intelligence Applicant Tracking System Resume Analysis Natural Language Processing Machine Learning Recruitment Automation

Cite This Article

Prasad Shriram Ghodake, Abhishek Dilip Gawate, Vaibhav Jivan Wadagale, Prof. Rahane D.A., "AI-BASED RESUME ATS SCORE ANALYZER," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-5, Jun 2026. https://doi.org/10.55041/IJSREM65349

Full Article/Conclusion

The AI-Based Resume ATS Score Analyzer shows how Artificial Intelligence can help with hiring. It solves problems with looking at resumes by doing it and giving smart suggestions. The system looks at resumes checks for words and meanings and gives a score that shows how good a candidate is for a job. It helps the people hiring to find the candidates and it also helps the candidates to make their resumes better. Overall the project works well. Gives us a system that is easy to use and helps both the people hiring and the candidates. It shows how AI can make hiring faster and more accurate.

References

1.                D.. J. H. Martin, Speech and Language Processing, 3rd Edition, Pearson Education 2023. 2.                J. Devlin, M. Chang, K. Lee and K. Toutanova "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding " NAACL-HLT, 2019. 3.                T. Mikolov et al. "Efficient Estimation of Word Representations in Vector Space " ICLR, 2013. 4.                F. Pedregosa et al. "Scikit-Learn: Machine Learning in Python " Journal of Machine Learning Research, 2011. 5.                S. Bird, E. Klein and E. Loper, Natural Language Processing with Python, Media, 2009. 6.                C. D. Manning, P. Raghavan and H. Schütze, Introduction, to Information Retrieval, Cambridge University Press, 2008. 7.                S.. P. Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson Education, 2020. 8.                OpenAI "Artificial Intelligence and Natural Language Processing Research " 2024. 9.                MongoDB Documentation, MongoDB Inc., Available: https://www.mongodb.com/docs 10.             spaCy Documentation, Available: https://spacy.io

Editorial & Review Transparency

Publication Date

6/23/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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