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 Article Faculty Publications -Articles 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 Sneha, Mishael; Raman, Gokulapriya; Vetriveeran, Divya Title A name given to the resource Intelligent predictive hiring model and personality assessment 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 International Journal of Work Organisation and Emotion;Volume;17;Issue;2026-03-01 00:00:00;pp.180-203 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJWOE.2026.153667" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJWOE.2026.153667</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039781243?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039781243?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 Sneha M., Department of Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Pondicherry, India; Raman G., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vetriveeran D., Anna University, University College of Engineering, Thirukkuvalai, India Description An account of the resource Selecting the right candidates is essential for organisational success, yet traditional hiring methods often fall short. This research introduces an advanced approach integrating natural language processing (NLP), personality assessment, and deep learning to improve candidate selection. NLP extracts key attributes from job descriptions and resumes, while personality assessments evaluate candidate suitability. A fusion of LSTM and RNN models predicts job fit using a dataset of job roles, resumes, and MBTI personality types. Pre-processing includes tokenisation, encoding, and data splitting for training and testing. The model architecture combines embedding layers with LSTM units, optimised using binary cross-entropy loss and accuracy metrics. Results show that this fusion model outperforms traditional algorithms, improving job matching accuracy. This research enhances recruitment by leveraging AI-driven insights. Future work will refine predictive models, integrate additional data, and address ethical concerns to ensure fairness and transparency, fostering a more efficient and equitable hiring process. Copyright 2026 Inderscience Enterprises Ltd. Subject The topic of the resource deep neural networks; intelligent hiring; natural language processing; NLP; personality assessment Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17408938; Language A language of the resource English Type The nature or genre of the resource Article 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