Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65306
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
In today's constantly changing job market, Human Resource (HR) departments face a significant challenge — reviewing and narrowing down hundreds of resumes for each job posting. The process of manually evaluating resumes is time-consuming, subjective, and often influenced by unconscious biases. This paper presents a Smart Resume Filter and AI HR Assistant, an end-to-end automation system built using Natural Language Processing (NLP) and Machine Learning (ML) to improve the recruitment process. The system automatically extracts information from resumes in PDF or DOCX formats, identifies key details such as education, experience, and skills, and matches them against job descriptions provided by HR. A custom ranking method assigns a relevance score to each candidate and displays the results on a user-friendly dashboard. The AI HR Assistant, integrated into the system, allows HR professionals to ask questions and get real-time insights. An evaluation using a dataset of 500 resumes showed a 92% accuracy in extracting data, 87% alignment with HR assessments in ranking, and a 72% reduction in screening time. This system greatly improves efficiency, consistency, and fairness in hiring decisions.
KEYWORDS
This research shows the potential of NLP and AI in transforming the HR recruitment process. The Smart Resume Filter and AI HR Assistant successfully automate resume screening, improve efficiency, and enhance decision quality. Experimental results confirm that the system reduces screening time by over 70% while maintaining high accuracy and fairness. With further improvements, this approach can form the basis for a fully intelligent HR recruitment system, aiding in real-time hiring decisions in large organizations.
1. Jain, A., & Singh, R. (2021). Automated Resume Screening using NLP and Machine Learning. International Journal of Computer Applications, 183(20), 1–6. 2. Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. 3. Brown, T., et al. (2020). Language Models are Few-Shot Learners. NeurIPS. 4. Kaur, R., & Patel, D. (2022). Semantic Resume Matching using BERT and TF-IDF. Journal of Data Mining and Knowledge Discovery, 14(2), 55–63. 5. Li, M., & Sharma, P. (2023). AI-Powered Recruitment Systems: Challenges and Trends. International Journal of AI and Data Science, 6(4), 122–138. 6. Arora, S., & Mehta, A. (2024). Automation in HR using Machine Learning and NLP. International Conference on Computational Intelligence.
Publication Date
6/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...