In an Al$-$based transient stability assessment$($TSA$)$ study for the IEEE $39-$bus test system in Figure $2,$ time domain simulations were performed using a software package. The data for each three$-$phase line fault in the test system were recorded, with samples stored in a database. Table $1$ details the input features for the Al$-$based TSA $($see the IEEE $39-$bus column$),$ which totals $150$ features. The Al$-$based TSA aims to classify whether the test system remains stable or becomes unstable after any credible contingencies. Discuss the impact of a large database and the number of input features on the Al system's performance, including training time and accuracy. In this scenario, it was observed that the Al training time is excessively long and the accuracy achieved is $75\%,$ whereas the required accuracy is $90\%$ or higher. Provide strategies to enhance the performance of the Al$-$based system. Additionally, explain how noise and outliers in the data can be identified and handled to improve the robustness of the Al model.