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Employee Attrition Prediction Using Deep Neural Networks
5 references·7 citing·33 second hop
Employee Attrition Prediction Using Deep Neural Networks (2021)Employee Attrition Prediction Using Deep …An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection (2021)An Improved Machine Learning-…Predicting Employee Attrition Using Machine Learning Techniques (2020)Predicting Employee Attrition…Predicting Employee Attrition using Machine Learning (2018)Predicting Employee Attrition…Artificial Intelligence, Economics, and Industrial Organization (2018)Artificial Intelligence, Econ…The Happy-Productive Worker Thesis Revisited (2008)The Happy-Productive Worker T…Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making (2025)Predicting Employee Attrition…Employee Retention Analytics: Predictive Modeling for Workforce Stability and Talent Management (2025)Employee Retention Analytics:…A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition (2025)A Deep Learning Model Based o…Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI (2024)Enhancing the Prediction of E…An Enriched Employee Retention Analysis System with a Combination Strategy of Feature Selection and Machine Learning Techniques (2023)An Enriched Employee Retentio…AI in HRM: case study analysis. Preliminary research (2023)AI in HRM: case study analysi…A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review (2023)A decade of research on machi…The Satisfaction With Life Scale (1985)Development and validation of brief measures of positive and negative affect: The PANAS scales. (1988)Development and validation of brief measures of positive and negative affect: The PANAS scales. (1988)High performance work systems, employee well-being, and job involvement: an empirical study (2016)Does Happiness Promote Career Success? Revisiting the Evidence (2018)Does Employee Happiness Have an Impact on Productivity? (2019)The Economic Implications of Learning by Doing (1962)The Economic Implications of Learning by Doing (1971)The Evolution of Cooperation (1985)How does artificial intelligence affect green economic growth?—Evidence from China (2022)Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects (2023)The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China (2023)The effects of pay level on organization‐based self‐esteem and performance: A field study (2004)An exploratory study of US lodging properties' organizational practices on employee turnover and retention (2009)Prediction of Employee Turnover in Organizations using Machine Learning Algorithms (2016)A Meta-Analysis of Antecedents and Correlates of Employee Turnover: Update, Moderator Tests, and Research Implications for the Next Millennium (2000)What really matters in a job? Hedonic measurement using quit data (2001)Human Resource Management and Labor Productivity: Does Industry Matter? (2005)Machine learning and role of artificial intelligence in optimizing work performance and employee behavior (2021)Predicting and explaining employee turnover intention (2022)Prediction of Employee Attrition Using Machine Learning and Ensemble Methods (2021)Unboxing the mystery: employee churn in the retail industry using machine learning approach (2024)Employee Turnover Prediction with Machine Learning: A Reliable Approach (2018)Consumer credit-risk models via machine-learning algorithms (2010)Surveying the forest: A meta‐analysis, moderator investigation, and future‐oriented discussion of the antecedents of voluntary employee turnover (2017)A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective (2021)Employee Attrition Prediction Using Bayesian Optimized Stacked Ensemble Learning and Explainable AI (2025)A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017)Unlocking the value of artificial intelligence in human resource management through AI capability framework (2022)Predicting Employee Attrition Using Machine Learning Approaches (2022)Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making (2023)From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction (2021)Predictive Model of Employee Attrition Based on Stacking Ensemble Learning (2022)
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References

  • 2008The Happy-Productive Worker Thesis Revisited413 cited
  • 2020Predicting Employee Attrition Using Machine Learning Techniques280 cited
  • 2018Artificial Intelligence, Economics, and Industrial Organization210 cited
  • 2018Predicting Employee Attrition using Machine Learning138 cited
  • 2021An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection86 cited

Cited by

  • 2023A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review62 cited
  • 2024Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI39 cited
  • 2025Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making38 cited
  • 2023An Enriched Employee Retention Analysis System with a Combination Strategy of Feature Selection and Machine Learning Techniques34 cited
  • 2025Employee Retention Analytics: Predictive Modeling for Workforce Stability and Talent Management30 cited
  • 2025A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition29 cited
  • 2023AI in HRM: case study analysis. Preliminary research27 cited

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