## 4 Discussion
The significance of the latter is quite important as the economic importance of intrusion detection systems expands, rendering them susceptible to future intrusion attacks. The authors utilized a conditional VAE with a distinct architecture that incorporated intrusion labels within the decoder layers. The proposed methodology exhibited several advantages, such as a lower level of complexity, high accuracy, and superior classification outcomes when compared to other well-known classifiers [27]. Another study utilized VAE in the area of choice modeling [28]. Choice modeling plays a pivotal role in transportation research, particularly in the areas of demand forecasting and infrastructure development. The process comprises two primary stages: the generation of a set of choices and the modeling of the decision-making process based on the provided choice set. The procedure of generating choice sets is very important as the inclusion or exclusion of certain options in the choice set can lead to inaccuracies in estimating parameters. The authors developed a generalized extreme value model that served to connect the value-added evaluation approach with choice modeling [28]. In another recent study, the authors applied a VAE model along with image processing methods in game design [29]. This study is considered to be the first to investigate various mathematical properties associated with VAE models. The VAE model demonstrated its proficiency in data clustering, and it was observed to be particularly efficient in generating images that exhibit a certain graphical structure or in managing and creating images that have low resolution demands [29]. Finally, another study presented a novel approach that combined a disentangled VAE with a bidirectional long short-term memory network backend in order to detect anomalies in heart rate data collected during sleep using a wearable device [30]. The performance of this model was compared with that of other established anomaly detection algorithms. It was shown that the developed model exhibited superior performance across a wide range of scenarios and with all participants under consideration [30]. In this study, VAEs are proposed as a data augmentation method in clinical trials, namely, as a way to reduce the required sample size. VAEs can be considered an extension of traditional autoencoders [11]. In contrast to autoencoders, variational autoencoders employ an encoder network that maps input data to a multivariate normal distribution, rather than a fixed point. In other words, VAEs strive to create a correlation between the input data and a probability distribution throughout the latent space [11; 12; 13]. The use of VAEs offers several advantages; for example, they have been identified as a highly effective and valuable approach for the development of generative models, namely, models that are utilized to produce novel synthetic or artificial data based on existing data. A notable benefit of VAEs in comparison to conventional autoencoders lies in their ability to generate novel data from the same embedding distribution as the input data through sampling from the embedded distribution. These unique features were exploited in this study in order to virtually increase the sample size of a clinical trial. This study showed that by applying VAE, it became feasible to use only 20% of the original dataset without changing the true outcome of the study (Figure 6a,b). It is worth noting that even for data exhibiting high variability (e.g., 40%), the use of a VAE can reduce the need for a sample size to less than half of the one typically needed for a trial (Figure 6c). This dramatic reduction in sample size can accelerate clinical trials, significantly reduce costs, and certainly diminish human exposure. To the best of our knowledge, this study represents the first attempt to employ autoencoders (and neural networks in a general sense) within the domain of clinical research aimed at reducing the necessary sample size.