In contrast to fully-connected neural networks, MCD presents an efficient technique for estimating predictive uncertainties while simultaneously mitigating the risk of overfitting, a common challenge in deep learning architectures with multiple hidden layers . MCD introduces stochasticity by randomly omitting neurons across every layer during both the training and prediction phases. This randomness ensures that the model does not overly rely on specific features of the training data, thereby enhancing its generalization capabilities. During the prediction process, dropout is applied in conjunction with the Monte Carlo method; by varying the neurons that are dropped in each iteration, a diverse set of predictions is generated. These predictions can then be aggregated to estimate a Gaussian distribution, characterized by its mean and standard deviation, providing a quantifiable measure of uncertainty. Additionally, the practice of neuron omission during training contributes to reduced training durations, offering computational efficiency.