## 3 Methodology and Proposed Model  
Recent developments in deep learning (DL) have demonstrated impressive promise for large-scale dataset analysis to uncover complex patterns, offering significant improvements in predictive accuracy for employee turnover. DL is the process of learning hierarchical data representations using a neural network structure that includes several hidden layers that add to the network's depth. In DL algorithms, data flow through these layers sequentially, with each layer gradually extracting increasingly complex characteristics and forwarding essential data to the next. Low-level characteristics are captured by the first layers, which are subsequently integrated and improved by later layers to produce a thorough and intricate depiction of the data. Deep learning methods, renowned for their ability to automatically recognize and learn features from training data, have demonstrated superior performance across various classification tasks. Thanks to its inherent feature extraction capabilities, DL has become a frontrunner in tackling complex challenges, demonstrating a significant edge in addressing sophisticated problems [45]. In this study, we propose a novel ensemble deep learning framework designed to predict employee attrition with high accuracy. At the heart of this framework lies the Bidirectional Temporal Convolutional Network (Bi-TCN), which excels in capturing complex correlations within the data, significantly improving classification performance. Figure 2 illustrates a comprehensive overview of the proposed methodology and model architecture. The ensemble consists of two Bi-TCN layers, each configured with distinct numbers of filters and kernel sizes to enhance feature extraction. In particular, 32 filters with a kernel size of 3 are used in the first Bi-TCN layer, while 64 filters with a kernel size of 5 are used in the second. This strategic utilization of varying kernel sizes and filter counts allows the model to capture a wider range of local dependencies, enabling a more comprehensive and detailed analysis of the data. Temporal Convolutional Networks (TCNs) can learn hierarchical feature representations because they use many convolutional layers with progressively larger receptive fields. Therefore, this method is very advantageous for non-sequential datasets with patterns across many feature dimensions. TCNs are also more successful on small or irregular datasets because they use weight sharing and dilation techniques, which decrease the number of trainable parameters and improve generalization. We also utilize a batch normalization layer after the fully connected layer, two dropout layers with a rate of 0.5 after each Bi-TCN layer, and a weight decay regularization (L2) technique in order to prevent overfitting in the proposed deep learning model. Weight decay encourages the weights to be modest in size, which enhances generalization and lowers the chance of overfitting [46]. Temporal Convolutional Network (TCN): One notable advancement in deep learning architectures is the Temporal Convolutional Network (TCN) [47], which excels at analyzing temporal patterns while retaining the robust feature extraction capabilities of Convolutional Neural Networks (CNNs). TCN is characterized by two key features: (1) By using causal convolutions, it guarantees that the output at any given time step is entirely dependent on the inputs that are now and previously present, with no effect from inputs that may be added in the future. (2) Like Recurrent Neural Networks (RNNs), TCN can handle sequences of any length and generate outputs that match the input sequence in length. The typical TCN architecture comprises three main components: residual connections, dilated convolutions, and causal convolutions. Causal convolutions are one-dimensional convolutional layers designed to use only data from time \(t\) and prior to that to compute the output at time \(t\).