Deep Learning and Optimization Approaches in A Parallel Convolutional Neural Network-Based Human Resources Recruitment System for Business Process Management Using Human Evolutionary Optimization Algorithm: A Review

Abstract

The integration of deep learning and optimization techniques has significantly transformed modern human resource recruitment systems, enabling intelligent decision-making and efficient talent acquisition processes. Parallel convolutional neural networks have emerged as a powerful deep learning architecture capable of extracting complex features from heterogeneous candidate data, including textual resumes, video interviews, and behavioral assessments. When combined with human evolutionary optimization algorithms, these systems achieve enhanced adaptability, improved parameter tuning, and superior predictive performance. This review paper presents a comprehensive analysis of recent advancements in deep learning and optimization approaches applied to parallel CNN-based recruitment systems within business process management frameworks. The study examines various architectural designs, hybrid models, and optimization strategies that contribute to improved recruitment accuracy and scalability. Additionally, it highlights the role of evolutionary algorithms in feature selection, hyperparameter optimization, and decision refinement. The paper also discusses key challenges such as data bias, interpretability, and computational complexity, along with emerging trends including automated HR analytics pipelines and explainable AI techniques. The findings provide valuable insights into the development of intelligent recruitment systems and offer guidance for future research directions in AI-driven human resource management.