AI and ML-Driven Blockchain-Based Secure Employee Data Management: Applications of Distributed Control and Tensor Decomposition in HRM

Abstract

Background Information: The historical information that provides context to a particular event or situation. It includes relevant details and facts that help explain why something is happening or how it came to be. This background helps to create a better understanding of the overall narrative or discussion. The incorporation of AI, ML, and Blockchain in human resource management (HRM) is altering the field by offering improved data security, predictive analysis, and automation. Conventional HR systems, which depend on centralized databases, are at risk of breaches and inefficiencies, highlighting the importance of advanced technologies for secure, scalable, and intelligent management of employee data. Objectives: The objective of this paper is to enhance HR data security, simplify processes, and improve decision-making through the utilization of AI/ML for predictive analytics, Blockchain for decentralized data security, and Tensor Decomposition for effective data management in large HRM systems. Methods: Techniques Varieties of ways. The suggested system merges Blockchain for decentralized, tamper-proof storage, AI/ML models for workforce forecasts, and Tensor Decomposition for handling multifaceted HR datasets. Smart contracts guarantee secure access management, while predictive models automate HR tasks, enhancing system efficiency and decision-making. Results: The combined system improves data security (0.99), prediction accuracy (0.95), and system efficiency (0.98), surpassing traditional HR techniques in securely and efficiently managing large-scale employee data. The results confirm the relationship between the variables examined and provide insight into the topic under investigation. Conclusion: AI, ML, Blockchain, and Tensor Decomposition work together to form a secure, scalable, and intelligent HRM system that automates important processes, enhances data security, and improves decision-making. This method shows substantial improvements compared to traditional HR systems.