International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Automatic Resume Screening Using SBERT

Author(s) Ms. Neha Rashi, Prof. Vishnu Mohan C
Country India
Abstract Recruitment processes in the modern era are swamped with a high volume of applications, making manual resume screening a significant bottleneck. To address these inefficiencies, this project presents the design and implementation of an intelligent system for the automatic screening and ranking of resumes.
The system puts forward an automated resume screening system leveraging Machine Learning (ML), and Natural Language Processing (NLP) to enhance recruitment efficiency and fairness. The system is architected to serve both recruiters, by enabling the ranking of multiple candidates, and applicants, by providing personalized feedback on their suitability for a role.
A feature vector is engineered using a pre-trained Sentence-BERT (SBERT) model for semantic comparison, complemented by keyword overlap and document statistics. This vector is then used to train a Random Forest Classifier, which predicts a probabilistic suitability score for each candidate. A key contribution is a novel course recommendation engine for applicants, powered by a Large Language Model (GPT-4o mini). model. The entire system is deployed via an intuitive, dual-portal Gradio web application, providing a practical tool for both recruiters and candidates.
Keywords Resume Screening, Natural Language Processing (NLP), Machine Learning, Semantic Similarity, Sentence-BERT, Large Language Models (LLM)
Field Computer
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-10
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.57538
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals