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 Rani, Mary Benitta; Praveena, F.; Talamala, Suresh; Paul, Gaddam Rahul; Vasanti, G.; Praveena, S. Title A name given to the resource Enhancing Human Resource Management With Fuzzy Logic and Neural Networks for Personalized Performance Management 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 CCIC 2026 - Contemporary Computing Innovations Conference 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CCIC68129.2026.11486118" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CCIC68129.2026.11486118</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038712434?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038712434?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 Rani M.B., College of Administrative and Financial Sciences, University of Technology, Bahrain; Praveena F., Mar Ephraem College of Engineering and Technology, Department of Master of Business Administration, Tamil Nadu, Kanyakumari, India; Talamala S., School of Business, Aditya University, Surampalem, Andhra Pradesh, Kakinada, India; Paul G.R., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Vasanti G., Aditya Institute of Technology and Management (Autonomous), Department of Basic Science and Humanities, Tekkali, Andhra Pradesh, Srikakulam, India; Praveena S., St. Joseph's College of Engineering, Department of Master of Business Administration, Tamil Nadu, Chennai, India Description An account of the resource Human resource management (HRM) encounters that is making more challenging some exact, fair and person related performance appraisals on a large scale. Traditional methods often fail to capture the richness of human behaviour and tend to be opaque to interpretation. The proposed study contributes a new hybrid approach of Fuzzy Logic and Neural Network for enhanced personalized performance management. The facts are presented qualitatively by the fuzzy inference system in linguistic terms while for numerical features, the neural network analyses such that it can find complex relationship patterns. This methodology ensures the simplicity and high predictability. The model trained on Kaggle dataset achieved an accuracy of 94.7%, F1-score of 0.942, precision of 0.945, recall of 0.940 and AUC-ROC of. 976 which were higher compared to baseline approaches like Logistic Regression and Decision Trees respectively. The solution helps HR professionals make sense of relevant information into employee performance and developmental needs, which are highlighted in real time. The results suggest that combining rule-based reasoning and machine learning enhances personalisation and offers more transparent human resources practices. This study provides a foundation for the next generation of intelligent HRM systems enabling adaptive decision support in various organizational settings. 2026 IEEE. Subject The topic of the resource Artificial Intelligence in HR; Data-Driven HRM; Decision Support; Employee Performance Prediction; Explainable AI; Fuzzy Logic; HR Analytics; Human Resource Management; Hybrid Model; Interpretability; Machine Learning; Neural Networks; Performance Metrics; Personalized Performance Evaluation; Rule-Based Systems Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-831952966-4; 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