**2. Methodology**

This work was carried out in two stages. First, the experiments were performed, where the data used for the machine learning implementation were generated. Second, these data were preprocessed and prepared for the deployment of machine learning tests and analysis. Velocity measurements were used to estimate acceleration, as described in the work done by Pietrasanta et al. [44]. The length of bubbles was also measured. Pressure measurements also took place in both thermal terminals of the device (i.e., condenser and evaporator). These measurements were used to estimate physical properties for the calculation of dimensionless numbers such as Reynolds (Re), Weber (We), Froude (Fr), and Bond (Bo) numbers, as defined in Pietrasanta et al. [29]. The labeling process was conducted visually while analyzing the high-speed images. These values, along with the label for each observation, were used to train the machine learning algorithms. Note that the entire set of data was split into a training and testing subsets. Cross-validation within the training set was also implemented for hyperparameter selection.
