Explica de forma profesional los resultados que se presentan el siguiente texto científico: "
\label{sec:Discussion}

In this section, we present the fundamental findings derived from our experiments with Red AI and Green AI. We have conducted a thorough analysis of the various experimental results, focusing on HPO, and key metrics such as performance, energy efficiency, and sustainability.

The results provide a detailed insight into how the variation in the number of trials influences both validation accuracy and environmental cost ($CO\textsubscript{2}e (g/Kwh)$). In both metrics, the Bayes Optimization Hyperband algorithm distinguished itself by its stability in achieving high validation accuracies and, simultaneously, exhibiting a comparatively low carbon footprint, being especially relevant in the context of Green AI.

In the experimentation of Red AI, BOHB and PBT proved to be the most effective in terms of accuracy with 10 trials, although BOHB maintained stability with 100 trials. On the other hand, the ASHA algorithm showed an improvement in average CO\textsubscript{2}e as the number of trials increased, suggesting that there may exist a trade-off between accuracy and sustainability that varies with the intensity of the optimization process.

In the context of Green AI, it is noteworthy that, while BOHB again demonstrated a suitable balance between accuracy and CO\textsubscript{2}e emissions in both sets of trials, the Hyperband (HB) algorithm stood out for its exceptional efficiency in terms of its very low CO\textsubscript{2}e emission with only 10 trials. However, this environmental performance did not hold when increasing the tests to 100 trials, which could infer a more energy-intensive optimization 
as the number of iterations increases.

These experimental results, focused on Red AI and Green AI, place emphasis on model performance and environmental impact. The models that achieved the highest validation accuracy were considered optimal for performance with unseen data. Among these, the models that recorded the lowest equivalent CO\textsubscript{2} emissions per kilowatt-hour stood out particularly for their environmental sustainability.

\begin{figure}[H]
\includegraphics[width=14cm]{Figures/Densidad}
\caption{\textcolor{blue}{The graph provides a visual representation of the distribution of the CO\textsubscript{2}e metric within the context of Green and Red Artificial Intelligence (AI) strategies. The results offer an in-depth view of the data density, depicted on both sides of an axis, thereby facilitating a more comprehensive understanding of the data distribution. Additionally, the figure displays the median, quartiles, and outliers of the data, offering an all-encompassing view of this metric's distribution across both AI strategies.}}
\label{fig:/Densidad}
\end{figure}

\textcolor{red}{The experimental results derived from both the Red AI and Green AI, specifically pertaining to CO\textsubscript{2}e emissions, and both utilizing the CIFAR-10 dataset, were subjected to analysis using the Mann-Whitney U test. The choice of a non-parametric test is based on the violation of the assumptions of normality and equality of variances, which are considered critical for the validity of parametric tests \cite{iantovics2018metrintsimil}. The necessity of non-parametric alternatives like the Mann-Whitney U test is underscored in such instances. A U value of 92407.0 was obtained, with a $p-value$ of $1.47 10^{-4}$. Given that this $p-value$ is less than 0.05, it can be concluded that there are statistically significant differences between the two strategies in hyperparameter optimization (Figure \ref{fig:/Densidad}).}

\textcolor{blue}{In the experimentation of the Red AI and Green AI strategies with the PBT and BOHB algorithms, using the IMDb dataset, the results do not meet the assumptions of normality and equality of variances. Therefore, the non-parametric Mann-Whitney U test was applied. In the case of the BOHB algorithm, the U value for CO\textsubscript{2}e emissions is 280.0, with a $p-value$ of 0.012, while for validation accuracy, U is 325.5 and the p-value is 0.067. These results indicate the presence of significant differences in  CO\textsubscript{2}e emissions between the Red AI and Green AI strategies (Figure \ref{fig:/Energy_IMDb}). However, in terms of validation accuracy, we cannot assert that there are significant differences between both strategies. On the other hand, in the case of the PBT algorithm, the U value for CO\textsubscript{2}e emissions is 589.0, with a $p-value$ of 0.041, while for validation accuracy, U is 461.0 and the $p-value$ is 0.877. Similar to the BOHB algorithm, these results indicate the presence of significant differences in  CO\textsubscript{2}e emissions between the Red AI and Green AI strategies. Nevertheless, in terms of validation accuracy, we cannot assert that there are significant differences between both strategies (Figure \ref{fig:/Accuracy_IMDb}).}

\begin{figure}[H]
\includegraphics[width=14cm]{Figures/Energy_IMDb}
\caption{\textcolor{blue}{The graph provides a visual representation of the distribution of the CO\textsubscript{2}e metric in the context of Green and Red Artificial Intelligence (AI) strategies for the IMDb dataset for the PBT and BOHB algorithms. The violin plot, when performing kernel density estimation, can produce negative values when smoothing the data, even though these negative values are not present in the data. The figure shows the median and quartiles, providing a comprehensive view of the distribution of this metric in both AI strategies.}}
\label{fig:/Energy_IMDb}
\end{figure}
"