Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Thiyagarajan, C.; Deepa, S.; Suguanthi, G. Meena; Sharmila, G. Title A name given to the resource Attention-Powered Deep Learning for Employee Analytics: A Multi-Model Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1771 LNNS;pp.132-144 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-14041-8_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-14041-8_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038754706?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038754706?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Attention Mechanisms; Data Preprocessing; Deep Learning; Employee Performance Evaluation; Human Resources Analytics; Machine Learning; Multi-Model Architectures; Neural Networks Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303214040-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online