Attention-Powered Deep Learning for Employee Analytics: A Multi-Model Approach

Title

Attention-Powered Deep Learning for Employee Analytics: A Multi-Model Approach

Creator

Thiyagarajan, C.; Deepa, S.; Suguanthi, G. Meena; Sharmila, G.

Description

In the ever-evolving field of human resources analytics, there is the integration of the latest techniques of machine learning that can strongly enhance decision-making. This paper introduces a revolutionary architecture for multi-model neural networks that integrate disparate networks in analyzing the background, development, performance, and engagement of an employee for all key elements of this employee. Each of the processes with attention fine-tunes the importance of features and therefore largely improves the concentration and interpretability of results. These networks are thus ensured of thorough analysis in the form of in-depth evaluation, which enables classification to be discrete and into clear performance categories. Preparation of raw data was also done with much care; we used the Employee/HR Dataset from Kaggle in order to process this raw data before its use in deep learning application. Our proposed architecture outperformed by accurately classifying the employee performance categories, with result showing a high classification accuracy of 86.49% on the test set. This study, therefore, establishes that customized neural network architectures are applicable in supporting organizations in realizing their data driven culture and in making human resource operations more efficient. 2026, Springer Science and Business Media Deutschland GmbH. All rights reserved.

Source

Lecture Notes in Networks and Systems;Volume;1771 LNNS;pp.132-144

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Attention Mechanisms; Data Preprocessing; Deep Learning; Employee Performance Evaluation; Human Resources Analytics; Machine Learning; Multi-Model Architectures; Neural Networks

Coverage

Thiyagarajan C., Department of Computer Science, PSG College of Arts and Science Coimbatore, Coimbatore, India; Deepa S., Department of Computer Science, CHRIST University, Bangalore, India; Suguanthi G.M., School of Management Sri Krishna College of Engineering and Technology, Coimbatore, India; Sharmila G., Department of Computer Applications, MLA Academy of Higher Learning, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303214040-1;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-14041-8_11

https://www.scopus.com/pages/publications/105038754706?origin=resultslist