Enhancing HR Analytics Using DNN-SVM Hybrid Model: A Strategic Approach to Decision-Making

Companies seeking to maximize human resource (HR) management in the competitive and fast-paced corporate environment of today find data-driven strategies increasingly vital. On the other hand, classic HR analytics methods sometimes find it difficult to strike accuracy, scalability, and interpretability when analyzing complex workforce data. This work presents a new hybrid model combining Support Vector Machine (SVM) and Deep Neural Network (DNN) in order to solve these challenges. While the DNN-SVM model uses the deep learning capabilities of DNN for the aim of feature extraction and pattern recognition, the SVM classification strengths are used for the aim of decision-making. Especially in areas including the evaluation of employee performance, recruitment, and employee turnover, the integration promises an increase in the accuracy of predictive analytics. In a comparison, the hybrid model performs better than both standalone DNN and SVM models in managing high-dimensional human resource data. Inspired by ideas of an MBA in human resource management, the study also investigates the practical implications of the DNNSVM model inside the framework of human resource management. Among the most important strategic applications are ones related to talent retention, leadership development, and workforce planning. Case studies from actual businesses show how this approach helps to make wise decisions, hence matching human resource objectives with business objectives. This study reveals a notable development in terms of accurate prediction, efficient prediction, and actionable insights. The DNN-SVM model guarantees sustainability and a competitive advantage since it provides human resource managers with a strong instrument to manage workforce problems. This research reveals the possibilities of hybrid artificial intelligence models to transform data-driven organizational developments and to revolutionize human resource analytics.