Deep Learning and Optimization Approaches in Convolutional Autoencoder with Dual-Key Transformer Network-Based Causality Analysis of Human Resource Practices on Firm Performance: A Review

Abstract

The integration of deep learning methodologies into human resource analytics has opened new avenues for understanding the causal relationships between HR practices and firm performance. This paper presents a comprehensive review of advanced techniques involving convolutional autoencoders combined with dual-key transformer networks for causality analysis in HR domains. Convolutional autoencoders facilitate efficient feature extraction and dimensionality reduction from complex HR datasets, while transformer architectures enhance contextual understanding through attention mechanisms. The dual-key transformer framework further strengthens data security and interpretability by enabling parallel processing of encrypted and contextual features. Optimization strategies, including evolutionary algorithms and gradient-based tuning, are critically analyzed for improving model performance and generalization. The study systematically reviews recent advancements, highlighting their contributions, methodologies, and performance metrics. Additionally, the paper explores challenges such as data heterogeneity, model interpretability, and scalability in organizational contexts. A graphical abstraction of the proposed framework is provided to illustrate the interaction between data preprocessing, feature extraction, transformer-based causality modeling, and decision outputs. This review aims to guide researchers and practitioners in selecting appropriate deep learning architectures for HR analytics and encourages future exploration in secure, explainable, and efficient AI-driven decision systems for organizational performance enhancement.