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<p>Human Resource Management and effective people management are essential for evaluating and improving employee performance to meet organizational goals. However, current studies lack a thorough analysis of how employee well-being, such as mental health and work-life balance affects performance. Integrating AI, ML, and ethereum blockchain technologies can enhance the accuracy of these evaluations. So, this paper proposed a employee performance prediction and management system using 2(LS)M based approach. Initially, data collected from performance prediction dataset is pre-processed, and time series is analyzed using HARR-BIMA. Then, features are extracted from both pre-processed and time series analyzed data, followed by feature selection utilizing DACOA. Next, 2(LS)M predicts employee performance, and deviation is analyzed using the Z-score. If the deviation is high, employee mental health data is collected and pre-processed, and features are extracted. Pearson Correlation Coefficient is used to analyze the correlation between extracted mental health and performance prediction features. Optimal features from the extracted mental health data are then selected using DACOA, and performance is managed using 2(LS)M based on the correlation and deviation analysis, and optimal features. Meanwhile, all the information from both pre-processed mental health and performance data are protected using KWTCHT and securely stored on the ethereum blockchain. In experimental analysis, the proposed 2(LS)M achieved an accuracy of 98.65% in predicting employee performance and 98.82% of accuracy in managing that performance.</p>

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AI and ML-driven employee performance management system with ethereum blockchain for human resource and people management using 2(LS)M approach

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Mohan Reddy Sareddy,
  • Gowdham C,
  • Manjula S,
  • Vinoth Kumar G,
  • Hemnath R,
  • Punitha Palanisamy

摘要

Human Resource Management and effective people management are essential for evaluating and improving employee performance to meet organizational goals. However, current studies lack a thorough analysis of how employee well-being, such as mental health and work-life balance affects performance. Integrating AI, ML, and ethereum blockchain technologies can enhance the accuracy of these evaluations. So, this paper proposed a employee performance prediction and management system using 2(LS)M based approach. Initially, data collected from performance prediction dataset is pre-processed, and time series is analyzed using HARR-BIMA. Then, features are extracted from both pre-processed and time series analyzed data, followed by feature selection utilizing DACOA. Next, 2(LS)M predicts employee performance, and deviation is analyzed using the Z-score. If the deviation is high, employee mental health data is collected and pre-processed, and features are extracted. Pearson Correlation Coefficient is used to analyze the correlation between extracted mental health and performance prediction features. Optimal features from the extracted mental health data are then selected using DACOA, and performance is managed using 2(LS)M based on the correlation and deviation analysis, and optimal features. Meanwhile, all the information from both pre-processed mental health and performance data are protected using KWTCHT and securely stored on the ethereum blockchain. In experimental analysis, the proposed 2(LS)M achieved an accuracy of 98.65% in predicting employee performance and 98.82% of accuracy in managing that performance.