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All Journal IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI)
Deshmukh, Asmita
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparing bidirectional encoder representations from transformers and sentence-BERT for automated resume screening Deshmukh, Asmita; Raut Dahake, Anjali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3404-3411

Abstract

In today’s digital age, organizations face the daunting challenge of efficiently screening an overwhelming number of resumes for job openings. This study investigates the potential of two state-of-the-art natural language processing models, bidirectional encoder representations from transformers (BERT) and sentence-BERT (S-BERT), to automate and optimize the resume screening process. The research addresses the need for accurate, efficient, and unbiased candidate evaluation by leveraging the power of these transformer-based language models. A comprehensive comparison between BERT and S-BERT is performed, evaluating their performance across multiple metrics, including accuracy, screening time, correlation with job descriptions, and ranking quality. The findings reveal that S-BERT outperforms BERT, achieving higher accuracy (90% vs. 86%), faster screening time (0.061 seconds vs. 1 second per resume), and stronger correlation with job descriptions (0.383855 vs. 0.1249). S-BERT though has a smaller vector size of 384 enables capturing richer semantic information compared to BERT’s vector size of 768, contributing to its superior performance. The study provides insights into the strengths and limitations of each model, offering valuable guidance for organizations seeking to streamline their talent acquisition processes and enhance candidate selection through automated systems.
Design and implementation of QUADRESCUE: A ROS-based quadruped robot for disaster response support Deshmukh, Sanjay; Chanakya, Ojas; Gabani, Om; Patni, Kashish; Deshmukh, Asmita
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i4.pp387-398

Abstract

Search and rescue (SAR) operations in hazardous environments demand robotic systems capable of traversing complex terrains while ensuring responder safety. Traditional wheeled platforms often fail in debris-laden areas, and fully autonomous quadrupeds remain financially out of reach for many rescue agencies. This paper presents the design and development of QUADRESCUE, a modular operator-assisted quadruped robot built to bridge the gap between affordability and capability in disaster response. QUADRESCUE delivers core SAR functionalities including remote visual inspection, real-time terrain mapping via an RGB-D camera, payload transport, and GPS-based survivor localization. Built with a robust three degrees of freedom (3DoF) per leg design, the robot uses inverse kinematics algorithms to precisely control twelve servo motors for stable locomotion across uneven terrain. The system integrates the robot operating system (ROS) for seamless operation, real-time joystick control for easy navigation, an IMU for orientation sensing, and a GPS module with 3-meter accuracy. Field evaluations demonstrate 80–94% success rates on challenging surfaces, substantially outperforming wheeled counterparts 19% to 39% with a 200-meter control range and 45 minutes of runtime. QUADRESCUE offers a lightweight, cost-effective, and repairable solution that combines practical usability with advanced performance, making it well-suited for real-world deployment in emergency rescue situations.
Scalable resume screening using large language model Meta AI version 3 Deshmukh, Asmita; Raut, Anjali; Deshmukh, Vedant
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp953-961

Abstract

This research paper explores the use of large language model Meta AI 3 (LLaMA 3) for automating the resume screening process. Traditional resume screening methods that rely on keyword searching and human review can be inefficient, biased, and fail to identify qualified candidates. LLaMA 3, trained on large-scale text datasets, has the potential to accurately analyze resumes by understanding context and semantic details beyond simple keyword matching.The study presents a system that converts resume PDFs to text, inputs the text along with the job description into the LLaMA 3 model, and generates a ranked list of candidates with reasoning for their job fit. This discusses the data preparation, model setup, and performance evaluation of this system. Results show LLaMA 3 can rapidly process batches of resumes while reducing human bias in the screening process. The system aims to streamline hiring by automating the initial resume screening stage to surface top candidates for further in-depth evaluation. Key benefits include improved accuracy in identifying relevant skills, reduced bias compared to human screeners, and significant time savings for recruiters. The paper also examines ethical considerations around using AI for hiring decisions. Overall, this work demonstrates the promising application of large language models (LLMs) like LLaMA 3 to transform and enhance resume screening practices.
Co-Authors Chanakya, Ojas Deshmukh, Sanjay Deshmukh, Vedant Gabani, Om Patni, Kashish Raut Dahake, Anjali Raut, Anjali