International Journal of Research and Development in Engineering Sciences (IJRDES ) | A Deep Learning Framework for Optimizing Talent Acquisition and Placement

A Deep Learning Framework for Optimizing Talent Acquisition and Placement

Talent acquisition and placement are critical processes in human resource management, yet they often face inefficiencies due to manual screening, bias, and mismatched hiring decisions. Finding suitable candidates for an open role could be a daunting task, especially when there are many applicants. It can impede team progress for getting the right person on the right time. In traditional models, they use ML technologies like KNN, and NLP on text based screen resuming which has limitations such as a bias, inefficiency and lack of personality assessment. To addressing this challenges we proposed a deep learning based framework for talent acquisition and placement using CNN and RNN. Comparing to the traditional models this system enhances improving accuracy, reduces recruiter workload to better workflows placement and talent acquisition. This enhances efficiency, improves decision-making, and ensures optimal talent placement in organizations.

Research Type: Longitudinal Research
Paper Type: Editorial Paper
Vol.7 , Issue 2 , Pages: 47 - 51, Mar 2025
Published on: 25 Mar, 2025
Issue Type: Regular

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

About Authors:

K.S.Shaheena

India

Viswam Engineering College

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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: K.S.Shaheena, shaeenakogatamshaik@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.