ItemConference Paper

Enhancing Human Resource Management With Fuzzy Logic and Neural Networks for Personalized Performance Management

Title
Enhancing Human Resource Management With Fuzzy Logic and Neural Networks for Personalized Performance Management
Creator
Rani, Mary Benitta; Praveena, F.; Talamala, Suresh; Paul, Gaddam Rahul; Vasanti, G.; Praveena, S.
Description
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.
Source
CCIC 2026 - Contemporary Computing Innovations Conference 2026;
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
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
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-831952966-4;
Format
online
Language
English
Type
Conference paper

Citation

Rani, Mary Benitta; Praveena, F.; Talamala, Suresh; Paul, Gaddam Rahul; Vasanti, G.; Praveena, S., “Enhancing Human Resource Management With Fuzzy Logic and Neural Networks for Personalized Performance Management,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25774.

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