Enhancing Enterprise Human Resource Management Through Intelligent Strategies Using Hybrid Deep Learning Models

John Paul M, Aurolipy Das, Pooja Goel, Lakshman, Malcolm Homavazir and Abhishek Upadhyay

Management (Montevideo), 2025, vol. 3, 170

Abstract: Introduction: Enterprise Human Resource Management (HRM), which tracks, evaluates, and improves employee performance, is essential to the expansion of a firm. However, conventional techniques like decision trees and linear regression frequently fall short in identifying intricate, non-linear relationships in employee data, which reduces their usefulness for making decisions in real time. Objective: The research aims to develop an intelligent, accurate, and scalable model for forecasting employee performance using a hybrid Deep Learning (DL) approach called the Intelligent Water Drops Driven Dynamic Long Short-Term Network (IntWD-DynLSTN). Methods: A real-world HR dataset that includes employee information like task completion rates, training hours, attendance, and performance ratings is used to train the algorithm. Preprocessing included encoding categorical data, using Z-score normalization, and addressing missing values by imputation. High-level characteristics were extracted from the structured HR data using Convolutional Neural Networks (CNN). These attributes were subsequently fed into a Dynamic Long Short-Term Network (DynLSTN) to identify sequential patterns in monthly employee performance. Finally, model hyperparameters were adjusted using the Intelligent Water Drops (IntWD) technique to enhance generality and accuracy. Results: Experimental results show that the proposed IntWD-DynLSTN model achieves higher prediction accuracy (0.98), precision (0.97), recall (0.97), and F1-score (0.98) compared to traditional and baseline methods. Conclusions: A scalable and dependable method for forecasting employee performance is provided by the suggested hybrid DL methodology. In dynamic organizational settings, it gives HR managers a strong tool for data-driven decision-making, facilitating quick interventions and efficient workforce management.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:manage:v:3:y:2025:i::p:170:id:1062486agma2025170

DOI: 10.62486/agma2025170

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