A Comprehensive Review of a Parallel Convolutional Neural Network-Based Human Resources Recruitment System for Business Process Management Using Human Evolutionary Optimization Algorithm

Abstract

The rapid transformation of business process management has significantly influenced human resource recruitment systems, necessitating the integration of intelligent computational models for efficient decision-making. This study presents a comprehensive review of parallel convolutional neural network-based recruitment systems enhanced by Human Evolutionary Optimization Algorithms. The proposed paradigm leverages parallelism in convolutional architectures to process large-scale candidate data, including resumes, behavioral traits, and skill metrics, thereby improving recruitment accuracy and efficiency. The integration of evolutionary optimization techniques inspired by human adaptive strategies enables dynamic tuning of model parameters, leading to enhanced convergence and reduced computational complexity. This review critically examines existing methodologies, highlighting the evolution of deep learning approaches in recruitment automation and their role in optimizing business workflows. Furthermore, the paper explores the synergy between parallel CNN frameworks and optimization algorithms in addressing challenges such as bias reduction, scalability, and real-time decision-making. The findings suggest that hybrid intelligent systems significantly outperform traditional recruitment methods by offering robust predictive capabilities and adaptability. This research contributes to the growing body of knowledge in artificial intelligence-driven human resource management and provides insights into future directions for developing intelligent, scalable, and efficient recruitment systems.