# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 4 Experimental Results
### Load Control Impacts  
Having in mind that each algorithm produces a different RMSE on the given dataset, based on the research of [PERSON] et al. (2024) [2], we provide an insight into the load deviation and efficiency of Load Control on public sector services. On a given scenario where CF = 1 and the Time Factor is also set to 1 min, provided the fact that each algorithm illustrates a dedicated RMSE, the new rescaled values of CF are as follows (Table 12): \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \begin{tabular}{l} **Linear** \\ **Regression** \\ \end{tabular} & \
\begin{tabular}{l} **ANN** \\ **(8 \(\times\) 4 \(\times\) 4)** \\ \end{tabular} & **ANFIS** & **GBM** &
\begin{tabular}{l} **Bagged** \\ **Dec. Trees** \\ \end{tabular} & **SVM** & **XGBoost** \\ \hline Mean Squared & & & & & & & & \\ Error (MSE) & 2963.599 & 1879.087 & 574.488 & 1649.868 & 1194.496 & 2963.599 & 7146.835 \\ \hline Root Mean Squared & & & & & & & \\ Error (RMSE) & 54.439 & 43.348 & 23.968 & 40.618 & 34.561 & 54.439 & 84.5389 \\ \hline Mean Absolute & & & & & & & \\ Error (MAE) & 40.530 & 32.078 & 15.064 & 31.758 & 26.167 & 40.530 & 65.026 \\ \hline Median Absolute & & & & & & & \\ Error (MedAE) & 32.210 & 24.655 & 7.849 & 25.623 & 19.792 & 32.210 & 49.720 \\ \hline Mean Squared Logarithmic & & & & & & & \\ Error (MSLE) & 0.008 & 0.005 & 0.001 & 0.004 & 0.003 & 0.008 & 0.01796 \\ \hline Root Mean Squared & & & & & & & \\ Logarithmic Error (RMSLE) & 0.087 & 0.069 & 0.0353 & 0.063 & 0.055 & 0.087 & 0.134 \\ \hline Mean Bias Deviation & & & & & & & \\ (MBD) & 0.010 & 0.004 & \(-\)0.001 & \(2.2611\times 10^{-13}\) & \(-\)0.172 & \(-\)0.010 & \(-\)14.770 \\ \hline Mean Absolute Error (MAPE—Validation) & & & & & & & \\ \hline Huber Loss & & & & & & & \\ Validation & 40.034 & 32.298 & 14.618 & 31.260 & 25.670 & 40.034 & 64.526 \\ \hline C-index & 0.70 & 0.80 & 0.88 & 0.88 & 0.90 & 0.70 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Algorithm metrics on training dataset. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \begin{tabular}{l} **Linear** \\ **Regression** \\ \end{tabular} & \
\begin{tabular}{l} **ANN** \\ **(8 \(\times\) 4 \(\times\) 4)** \\ \end{tabular} & **ANFIS** & **GBM** &
\begin{tabular}{l} **Bagged** \\ **Dec. Trees** \\ \end{tabular} & **SVM** & **XGBoost** \\ \hline Mean Squared & & & & & & & \\ Error (MSE) & 2781.079 & 1879.087 & 1284.383 & 2735.082 & 2589.088 & 2939.308 & 6663.567 \\ \hline Root Mean Squared & & & & & & & \\ Error (RMSE) & 52.736 & 43.348 & 35.838 & 52.298 & 50.883 & 54.2154 & 81.630 \\ \hline Mean Absolute & & & & & & & \\ Error (MAE) & 36.905 & 32.078 & 26.733 & 39.768 & 36.896 & 37.830 & 62.235 \\ \hline Median Absolute & & & & & & & \\ Error (MedAE) & 23.322 & 24.655 & 20.669 & 28.902 & 28.546 & 28.068 & 92.346 \\ \hline Mean Squared Logarithmic & & & & & & & \\ Error (MSLE) & 0.007 & 0.005 & 0.002 & 0.006 & 0.006 & 0.007 & 0.0164 \\ \hline Root Mean Squared Logarithmic Error (RMSLE) & & & & & & & \\ Logarithm (RMSLE) & 0.085 & 0.069 & 0.052 & 0.081 & 0.080 & 0.088 & 0.128 \\ \hline Mean Bias Deviation & & & & & & & \\ Error (MAPE) & 0.008 & 0.004 & 0.003 & 2.710 & 2.574 & \(-\)0.005 & \(-\)0.372 \\ \hline Mean Absolute Percentage Error (MAPE—Validation) & & & & & & & \\ \hline Huber Loss & & & & & & & \\ (Validation) & 5.77\% & 5.018\% & 3.797\% & 6.080\% & 5.732\% & 5.879\% & 9.68\% \\ \hline Huber Loss & & & & & & & \\ (Validation) & 36.410 & 32.2982 & 26.238 & 39.273 & 36.401 & 37.336 & 61.738 \\ \hline C-index & & & & & & & \\ \hline C-index & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Algorithms metrics on validation dataset. The following graphs (Figure 14) are deployed based on the slow-start algorithm, where cognitive switch produces a production loss of 20% on a time frame of 120 min and initial task queue of 40 tasks in TW1 in accordance with [PERSON] et al. (2024) [2], while Table 13 provides statistics on unfinished tasks and Node Load for the same time frame. In Figure 14a, 11 tasks remained unfinished.