### Intuition of Artificial Neural Networks  
Artificial Neural Networks (ANN) belong to the sphere of supervised Machine Learning (ML), which in turn falls under the wider concept of Artificial Intelligence (AI) [14]. The cornerstones of ANNs are their architecture and training data. In terms of architecture, each ANN comprises multiple layers of interconnected nodes. These include the input layer (input nodes), responsible for receiving and offering the input data to the network; the output layer (output node), which represents the final prediction of the network; and the hidden layer(s) in between, which capture and factor the various features present in the available dataset. In the case of Deep Learning, the ANN category that the present study falls under, the networks involve more than one hidden layer. These are fully connected; all nodes of one layer are connected to all nodes of the previous and following layers. The structure of ANNs resembles the structure of the human brain. Each node, mentioned above, represents a neuron, and the connections between those perform similar functions to brain synapses, as seen in Figure 1. Each neuron represents a feature of the training data and can be outlined by Equation (1), where f(x) is an appropriately selected activation function [15,16]:  
\[\mathrm{P}=\mathrm{f}\ \left(\sum_{i=1}^{n}w_{i}x_{i}+\mathrm{b}\right) \tag{1}\]  
where P is the output figure of the neuron, \(w_{i}\) is the weights applied to each feature of the data that are fed to the neuron, \(x_{i}\) is the features of the input data themselves, and b is the applied bias. Each neuron is initially assigned a random weight, quantifying the significance of the represented feature to the value of the dependent variable. These weights are constantly updated and ultimately optimised through an iterative training process (epoch). In each training loop, the contribution of each neuron to the dependent variable and the overall accuracy of the ANN algorithm prediction against a known value is evaluated. The process of comparing the network predictions against known values (the ground truth) is the founding principle of supervised learning [17]. To enable this internal assessment and feedback process, the use of a loss function at the end of each epoch is necessary. This feedforward, backpropagation training process is illustrated schematically in Figure 2.  
Similar to the architecture of the network, the choice of an appropriate loss function, along with parameters including, but not limited to, the activation function, number of epochs, learning rate, and batch size, is dependent on the type, quantity, and quality of the  
Figure 1: Graphical representation of the neurons the ANN consists of. The illustrated activation function is indicative only and represents the one used as part of this study. Figure 2: Graphical representation of the feedforward and backpropagation process of a typical ANN. input data and type of expected predictions [18]. These are called hyperparameters, and in conjunction with the ANN structure (number of hidden layers and number of neurons on each hidden layer) turn an ANN algorithm into a trainable and functioning predictive model. There is no prescriptive method for standardising the process of ANN development at the moment, in terms of hyperparameters and architecture selection [19]. However, previous scientific studies summarise useful conclusions that provide some guidance in this respect. Apart from developing an effective architecture and choosing appropriate hyperparameters, an aspect that requires attention is network overfitting [1]. Although an algorithm can perform extremely well in making predictions on the given training and test set, there is the risk of excessive focus on the provided observations, leading to the reduced general applicability of the model. Overfitting limits the scope of use of an artificial neural network and can potentially lead to misleading prediction results.