# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 4 Experimental Results
### Comparing Algorithms
#### 4.1.2 ANN Results  
As far as ANN is concerned, five different configurations were proposed:  
* Configuration 1: single hidden layer (10 neurons).
* Configuration 2: two hidden layers (8, 4 neurons).
* Configuration 3: three hidden layers (8, 4, 4 neurons).
* Configuration 4: three hidden layers (8, 8, 8 neurons).
* Configuration 5: three hidden layers (20, 20, 20 neurons).  
Configuration 3, with three hidden layers of 8, 4, and 4 neurons, demonstrated the best performance in terms of RMSE (43.34), indicating a good balance between prediction accuracy and model complexity. It also had a relatively low MAE and MedAE, suggesting accurate predictions and lower variability. Configuration 4, with three hidden layers of 8, 8, and 8 neurons, also performed well with a competitive RMSE (54.79). It maintained a balance between accuracy and model complexity. Configuration 1, which included a single hidden layer with 10 neurons, provided reasonable performance but fell slightly short compared to Configuration 3 regarding RMSE. Configuration 2, with two hidden layers of 8 and 4 neurons, demonstrated the second-highest RMSE. Configuration 5 demonstrated the worst performance with three hidden layers of 20, 20, and 20 neurons. In an attempt to add even more layers and nodes, the results showed that the corresponding values do not show improvement on any of the metrics mentioned, as seen in Table 4. For the remainder of the current chapter, we present the data extracted from Configuration 3 (Config. 3), which provides the best results compared to all configurations.  
Figure 4: Linear Regression analysis Time Factor.  
Figure 5(a) shows the ANN's performance in predicting 'Time Factor' during training, with each point representing a data observation. Figure 5(b) depicts the relationship between actual and predicted 'Time Factor' values during the testing phase. Figure 5(c) illustrates the ANN's prediction accuracy in the validation phase.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metric** & **Config. 1** & **Config. 2** & **Config. 3** & **Config. 4** & **Config. 5** \\  & **(10n)** & **(8n \(\times\) 4n)** & **(8n \(\times\) 4n)** & **(8n \(\times\) 8n)** & **(20n \(\times\) 20n)** \\ \hline Mean Squared Error (MSE) & 2698.985 & 3458.918 & 2285.915 & 3002.731 & 4108.571 \\ \hline Root Mean Squared Error (RMSE) & 51.951 & 58.813 & 47.811 & 54.797 & 64.098 \\ \hline Mean Absolute Error (MAE) & 39.303 & 42.670 & 35.397 & 39.592 & 46.205 \\ \hline Median Absolute Error (MedAE) & 30.637 & 31.223 & 26.198 & 25.815 & 36.911 \\ \hline Mean Squared Logarithmic Error (MSLE) & 0.006 & 0.009 & 0.005 & 0.008 & 0.012 \\ \hline Root Mean Squared Logarithmic Error (RMSE) & 0.083 & 0.094 & 0.075 & 0.088 & 0.109 \\ \hline Mean Bias Deviation (MBD) & \(-\)4.786 & 3.397 & \(-\)0.130 & \(-\)1.928 & 0.505 \\ \hline Mean Absolute Percentage Error (MAFE—Validation) & 6.18\% & 6.69\% & 5.47\% & 6.25\% & 7.21\% \\ \hline Huber Loss (Validation) & 38.807 & 42.170 & 34.897 & 39.092 & 45.707 \\ \hline Angle between Perfect Fit and Best Fit (degrees) & 29.236 & 26.348 & 31.074 & 25.493 & 27.234 \\ \hline C-index & 0.70 & 0.75 & 0.85 & 0.80 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANN configurations with metrics for validation data.  
Figure 5: Configuration 3. ANN Time Factor performance for three layers of \(8\times 4\times 4\) neurons.  
The ANN model displayed significant improvement over Linear Regression, effectively capturing non-linear relationships in the data and yielding better predictive performance. The model's ability to learn from data through multiple layers of neurons enabled it to handle intricate patterns (Figures 6 and 7). Nonetheless, its training process was more computationally intensive compared to simpler models.