# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 6 Results
### Experiment Red AI with CIFAR
The optimized hyperparameters included learning rates (Irs) of 0.001, 0.00125, and 0.0015; epochs ranging from 5 to 20 iterations; optimizers such as Adam (AD), RMSprop (Rprop), and SGD; and batch sizes of 16, 32, and 64.  
crucial to underscore that the median \(CO_{2}e\) emissions for PBT are significantly elevated (1.222 g/kWh), suggesting the existence of outlier hyperparameter values that skew the mean. Pertaining to validation accuracy, the BOHB algorithm surpasses the others with a mean of 75.851% and a median of 77.420%. This implies that BOHB manifests superior consistency in terms of accuracy performance relative to the other algorithms evaluated. In the analysis of the various algorithms employed in both the Red AI and Green AI strategies, the Kruskal-Wallis test is utilized within each group. This non-parametric method is used to compare three or more independent samples. The Kruskal-Wallis test provides a flexible and robust alternative to parametric tests, as it does not require data normality (that the samples belong to a Gaussian distribution) [78; 79]. The Kruskal-Wallis test, applied to 10 trials of \(CO_{2}e\) in Red AI, yielded an H = 2.917 and a \(p\) = 0.4047. As this exceeds the conventional threshold of 0.05, no significant differences were found in the \(CO_{2}e\) medians among the evaluated algorithms. Similarly, for validation accuracy, with an H = 2.372 and a \(p\) = 0.499, no significant differences were found among the distributions of the studied algorithms. In the experimentation involving 100 trials, as depicted in Figure 2 and detailed in Table 3, in terms of energy efficiency, BOHB appears to be the most efficient, with a mean of 0.969 g/kWh of \(CO_{2}e\) emissions, closely followed by ASHA with a mean of 1.035 g/kWh. However, it is crucial to underscore that the median of the \(CO_{2}e\) emissions for both algorithms is lower than the mean, suggesting the presence of outlier values that could skew the mean. With respect to validation accuracy, HB outperforms the others with a mean of 74.555% and a median of 76.045%. This suggests that HB demonstrates greater consistency in terms of accuracy performance compared to the other algorithms, within the context of the experimentation involving 100 trials. The Kruskal-Wallis test, applied to 100 trials of the Red AI, found no significant differences in \(CO_{2}e\) emissions among the evaluated algorithms (H = 5.332, \(p\) = 0.149). Similarly, no differences were found in the validation accuracy among the algorithms (H = 4.321, \(p\) = 0.229). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{\(CO_{2}e\) (g/kWh)} & \multicolumn{4}{c}{**Val Accuracy**} \\ \cline{2-10}  & **Std** & **Var** & **Avg** & **Median** & **Std** & **Var** & **Avg** & **Median** \\ \hline ASHA & 0.539 & 0.290 & 0.926 & 0.868 & 9.486 & 80.984 & 72.036 & 73.915 \\ BOHB & 0.578 & 0.334 & 1.218 & 1.169 & 4.701 & 19.886 & 75.851 & 77.420 \\ HB & 1.132 & 1.281 & 1.563 & 0.738 & 10.238 & 94.326 & 70.956 & 70.280 \\ PBT & 0.447 & 0.200 & 0.854 & 1.222 & 9.078 & 74.169 & 70.502 & 71.970 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performance of algorithms in Red AI with 10 trials. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{\(CO_{2}e\) (g/kWh)} & \multicolumn{4}{c}{**Val Accuracy**} \\ \cline{2-10}  & **Std** & **Var** & **Avg** & **Median** & **Std** & **Var** & **Avg** & **Median** \\ \hline ASHA & 0.770 & 0.592 & 1.035 & 0.819 & 7.806 & 60.930 & 72.614 & 74.585 \\ BOHB & 0.703 & 0.494 & 0.969 & 0.763 & 8.454 & 71.477 & 72.687 & 75.850 \\ HB & 0.864 & 0.746 & 1.202 & 0.993 & 5.392 & 29.072 & 74.555 & 76.045 \\ PBT & 0.795 & 0.633 & 1.156 & 0.958 & 8.179 & 66.888 & 72.255 & 74.395 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative performance of algorithms in Red AI with 100 trials.