## 3 Results
Without proper tuning, deep models can suffer from underfitting, overfitting, or unstable convergence--issues that directly impact classification accuracy and generalization. Parameters such as learning rate, dropout rate, hidden layer size, activation functions, and transformer depth significantly influence the training dynamics and decision boundaries. In the proposed model, the optimization of these hyperparameters was handled using the IChOA, which adaptively explored the parameter space through a dynamic balance of exploitation and exploration. This allowed the model to reach near-optimal configurations that enhanced convergence speed, detection performance, and architectural stability across diverse IIoT datasets. Table 1 summarizes the tuned hyperparameters for the proposed Transformer-GAN-AE and all baseline models. The proposed model employed IChOA for fine-tuning key architectural components such as the number of attention heads (8), encoder layers (6), feedforward hidden size (2048), and dropout rate (0.2). The IChOA optimizer dynamically regulated learning rate (0.003), convergence threshold (0.059), momentum (0.03), and latent dimensions, resulting in a balanced and stable configuration. In contrast, the baseline models, including GAN, CNN, DBN, TST, BERT, and XGboost, were tuned using grid search, a brute-force method that systematically tests combinations from a predefined set of hyperparameter values. While grid search is simple and widely used, it is computationally expensive and lacks adaptive search behavior, often missing fine-grained optima that stochastic optimizers such as IChOA can capture. A closer look at Table 1 reveals distinct patterns. For instance, the GAN baseline achieved its best performance using a learning rate of 0.005, a batch size of 64, and ReLU activation with SGD optimization. TST benefited from a relatively high learning rate (0.037) and Adam optimizer, reflecting its sensitivity to gradient flow in attention mechanisms. BERT was tuned with conservative parameters, including a lower dropout rate (0.1) and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline \hline \multirow{8}{*}{Transformer-GAN-AE} & Learning rate & 0.003 \\  & Batch size & 32 \\  & Feed forward hidden size & 2048 \\  & Weight decay & 0.01 \\  & Dropout rate & 0.2 \\  & Number of attention heads & 8 \\  & Number of encoder layers & 6 \\  & Activation function & GELU \\  & Optimizer & IChOA \\  & Momentum term & 0.03 \\  & Convergence threshold & 0.059 \\  & Hidden Layer Sizes & 60 \\  & a & \([-1,1]\) \\  & Population size & 100 \\  & Iteration & 300 \\ \hline \multirow{8}{*}{GAN} & Learning rate & 0.005 \\  & Number of neurons in hidden layers & 32 \\  & Batch size & 64 \\  & Momentum term & 0.06 \\  & Activation function & ReLU \\  & Convergence threshold & 0.063 \\  & Optimizer & SGD \\ \hline \multirow{8}{*}{BERT} & Learning rate & 0.002 \\  & Batch size & 32 \\  & Dropout rate & 0.1 \\  & Number of self-attention heads per layer & 6 \\  & Number of transformer encoder layers & 6 \\  & Length of input time-series window & 64 \\  & Activation Function & GELU \\  & Optimizer & SGD \\ \hline \multirow{8}{*}{TST} & Learning rate & 0.037 \\  & Number of attention heads & 6 \\  & Number of transformer layers & 6 \\  & Dropout rate & 0.2 \\  & Optimizer & Adam \\ \hline \multirow{8}{*}{CNN} & Number of convolution layers & 10 \\  & Kernel size & \(5\times 5\) \\  & Pooling type & Max pooling (\(2\times 2\)) \\  & Number of neurons & 64 \\ \hline \multirow{8}{*}{DBN} & Number of hidden layers & 6 \\  & Number of neurons in hidden layers & 32 \\ \cline{1-1}  & Learning rate & 0.009 \\ \cline{1-1}  & Activation & Tanh and sigmoid \\ \cline{1-1}  & Optimizer & SGD \\ \hline \multirow{2}{*}{XGBoost} & Learning rate & 0.29 \\ \cline{1-1}  & Max depth & 6 \\ \cline{1-1}  & Number of estimators & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter setting of proposed methods. SGD, which is consistent with its stable, deep encoding nature.