A Survey of Methods and Architectures for A Parallel Convolutional Neural Network-Based Human Resources Recruitment System for Business Process Management Using Human Evolutionary Optimization Algorithm

Abstract

The integration of artificial intelligence in human resource management has significantly transformed recruitment processes within business process management systems. This study presents a comprehensive survey of methods and architectures centered on a parallel convolutional neural network-based recruitment framework enhanced by a human evolutionary optimization algorithm. The proposed paradigm leverages parallel CNN architectures to extract multi-dimensional candidate features such as textual resumes, behavioral indicators, and skill-based attributes, enabling efficient and scalable decision-making. The incorporation of human evolutionary optimization further refines model parameters through adaptive learning inspired by human cognitive and evolutionary strategies, thereby improving prediction accuracy and robustness.

This survey systematically reviews existing methodologies, including deep learning-based recruitment models, optimization-driven decision systems, and hybrid AI frameworks. It highlights the strengths and limitations of current approaches while emphasizing the importance of parallelization in handling large-scale recruitment data. Additionally, the study explores how business process management benefits from intelligent recruitment automation through enhanced efficiency, reduced bias, and improved candidate-job matching.

The findings indicate that combining parallel CNN architectures with evolutionary optimization algorithms significantly enhances recruitment performance metrics such as accuracy, precision, and scalability. The paper concludes by identifying research gaps and future directions, including explainable AI, ethical considerations, and real-time adaptive recruitment systems.