Artificial Intelligence-Based Parallel Convolutional Neural Networks for Human Resource Recruitment Using Human Evolutionary Optimization

Abstract

The integration of artificial intelligence in human resource management has significantly transformed recruitment systems by enhancing decision-making accuracy and operational efficiency. This study presents a comprehensive exploration of a parallel convolutional neural network-based recruitment framework optimized using a human evolutionary optimization algorithm for business process management. The proposed approach leverages parallel CNN architectures to extract multi-dimensional features from candidate data, including resumes, behavioral assessments, and interview transcripts. The incorporation of human evolutionary optimization enables dynamic parameter tuning, improving model convergence, generalization, and selection accuracy. This paper investigates current trends in AI-driven recruitment, highlighting advancements in deep learning, optimization strategies, and intelligent decision support systems. Furthermore, it examines key challenges such as data bias, ethical concerns, scalability, and interpretability. The study aims to bridge the gap between intelligent automation and human-centric decision-making in recruitment processes. By synthesizing recent developments and evaluating methodological innovations, this research contributes to the development of robust, scalable, and fair recruitment systems aligned with modern business process management requirements. The findings provide valuable insights for researchers and practitioners seeking to implement advanced AI techniques in HR analytics.