###### Abstract  
Employee attrition, which causes a significant loss for an organization, is the term used to describe the natural decline in the number of employees in an organization as a result of numerous unavoidable events. If a company can predict the likelihood of an employee leaving, it can take proactive steps to address the issue. In this study, we introduce a deep learning framework based on a Bidirectional Temporal Convolutional Network (Bi-TCN) to predict employee attrition. We conduct extensive experiments on two publicly available datasets, including IBM and Kaggle, comparing our model's performance against classical machine learning, deep learning models, and state-of-the-art approaches across multiple evaluation metrics. The proposed model yields promising results in predicting employee attrition, achieving accuracy rates of 89.65% on the IBM dataset and 97.83% on the Kaggle dataset. We also apply a fully connected GAN-based data augmentation technique and three oversampling methods to augment and balance the IBM dataset. The results show that our proposed model, combined with the GAN-based approach, improves accuracy to 92.17%. We also applied the SHAP method to identify the key features that most significantly influence employee attrition. These findings demonstrate the efficacy of our model, showcasing its potential for use in various industries and organizations. employee attrition; employee turnover; deep learning model; bidirectional temporal convolutional network (Bi-TCN); data augmentation; generative adversarial network (GAN); explainable AI (XAI)  
2025152984202  
Employee attrition can be classified into two distinct categories: involuntary and voluntary. Involuntary attrition occurs when employees are terminated due to reasons such as poor performance or organizational restructuring. Conversely, voluntary attrition refers to situations where high-performing employees choose to leave the organization, often despite efforts by the company to retain them [3]. Employee attrition can pose a significant challenge to an organization's competitive advantage, leading to a variety of negative consequences. This issue has garnered considerable attention across industries, primarily due to its detrimental impact on organizational performance, operational efficiency, and the continuity of long-term growth initiatives. At-trtition can incur substantial costs, as companies must devote extra resources for hiring, and training of new staff [4; 5]. Companies must dedicate a lot of resources and time to training each employee according to the company's needs. When an employee leaves, not only does the organization lose valuable talent, but it also forfeits the considerable investments made in recruitment, screening, and training. Consequently, to fill vacancies, the company must once again dedicate significant resources to hiring, training, and integrating new staff members, perpetuating the cycle of cost and disruption [6]. Figure 1 illustrates the key aspects of human resources (HR) management and the impact of employee attrition on these areas. Given these challenges, minimizing employee turnover becomes a central objective for organizations striving to maintain a stable and productive workforce. Companies can mitigate the negative effects of attrition on performance, morale, and overall business outcomes by fostering more engaging work environments and implementing effective organizational policies. By adopting targeted strategies to reduce attrition, businesses can not only lower the costs associated with recruitment and training but also enhance their long-term competitive edge and improve workforce stability [2]. In the contemporary digital landscape, business strategies are increasingly shaped by the integration of cutting-edge technologies like artificial intelligence (AI), machine learning (ML), and deep learning (DL). These technologies have evolved beyond their initial supporting roles, now playing a central role in contemporary commercial systems.