Recent Advances in A Parallel Convolutional Neural Network-Based Human Resources Recruitment System for Business Process Management Using Human Evolutionary Optimization Algorithm: A Systematic Review

Abstract

The rapid evolution of artificial intelligence has significantly transformed human resource recruitment systems, enabling more efficient, accurate, and scalable decision-making processes. In recent years, parallel convolutional neural networks have emerged as a powerful approach for analyzing large-scale candidate data by extracting multi-dimensional features simultaneously. When integrated with human evolutionary optimization algorithms, these systems demonstrate enhanced adaptability, optimization capability, and decision accuracy in complex recruitment environments. This systematic review explores recent advances in the development and application of parallel convolutional neural network-based recruitment systems within business process management frameworks. The study examines key methodologies, architectural innovations, optimization strategies, and performance improvements reported in contemporary research. Emphasis is placed on how evolutionary optimization techniques contribute to model tuning, feature selection, and decision optimization, ultimately improving hiring outcomes. Additionally, this review highlights challenges such as data bias, model interpretability, and scalability issues while identifying emerging trends including hybrid AI models and automated HR analytics pipelines. The findings provide valuable insights into the integration of deep learning and evolutionary algorithms for intelligent recruitment systems, offering guidance for future research and industrial implementation. This paper aims to serve as a comprehensive reference for researchers and practitioners seeking to understand and advance AI-driven recruitment systems within modern business environments.