Advanced predictive modeling for employee attendance: Leveraging big data to optimize workforce management

Employee attendance is a significant challenge for organizations, often resulting in reduced productivity and increased operational costs. Traditional methods for predicting attendance struggle to account for complex patterns and high-dimensional data, which limits their effectiveness in proactive workforce management. To overcome this drawback, the precise proposes a novel approach that leverages Krill Herd-Improved Long Short-Term Memory (KH-ILSTM) to enhance attendance prediction accuracy. Data Collection includes gathering historical attendance records, work patterns, residence distance, and facial images of employees. The preprocessing method involves image resizing, to increase the visual quality of input images and Z score normalization to standardize continuous variables. Linear Discriminant Analysis (LDA) is used for dimensionality reduction, preserving critical discriminative features while reducing computational complexity. The proposed KH-ILSTM model allows the model to learn temporal patterns in attendance data while optimizing hyperparameters to improve predictive accuracy. Experimental results demonstrate accuracy (95.7%), precision (94.7%), and Recall (94.8%). Research contemplates that by integrating DL techniques with big data analytics, the proposed KH-ILSTM model significantly improves absenteeism predictions, offering a scalable and data-driven solution for optimized workforce management. Furthermore, organizations can leverage this model to analyze employee behavioral trends, identify underlying absenteeism patterns, and implement data-driven strategies for workforce optimization. The KH-ILSTM model's ability to handle vast amounts of data efficiently ensures its applicability across various industries. Future research can explore real-time data integration and adaptive learning techniques to further enhance the model’s robustness. By adopting AI-driven approaches, organizations can proactively address absenteeism-related challenges, leading to improved productivity and better resource allocation.