## 3 Materials and Methods
### Creating a Predictive Model--Digital Data Treatment  
Creating a predictive model, based on an artificial neural network (ANN), multilayer Perceptron (MLP), was preceded by structuring a set of the research data into input/output vectors in an Excel file. Out of a total of 71,053 collected input data, the software detected and removed those with unusual values (equal to zero) and with missing values of variables. Thus, according to the _supervised learning_ paradigm, 64,301 inputs/output vectors were included in the finally structured set adapted for training and testing of the predictive model. Supervised learning, as a machine learning technique, implies the existence of a data set of the input \(x\) and the corresponding output \(y\), for training so that the created model of classification (\(y\) can predict class \(x\)) or regression (\(y\) can have values in continuity) can predict previously \"unseen\" inputs. In LTE networks, as components of modern cyber-physical integrations of physical and digital processes, self-configuration of the software and adaptive devices of machines is performed and maintained by applying corrective decisions [24] on the principles of supervised machine learning. The collection of the input data about the physical processes is performed by sensors, while the information processing of a data set or machine learning of a model is performed by the ANN application through a layered architecture of a series of process elements--interconnected artificial neurons. The way in which an artificial neuron processes the signals is defined by a _transfer function_ that is a combination of the _activation_ and _output_ functions. The activation function maps the individual input signals in a neuron into a single composite signal, while the output function maps the value of the activation function into the output value of the neuron. The most common type of the output function is the _sigmoid function_, which limits the value of the activation function to the interval between 0 and 1. Each synapse, the connection between the neurons, is determined by its weight factor, and during the learning process they are adjusted in learning the function. Modification of these adjustable parameters allows the network to learn a certain law of mapping the vectors from the input space \(X\) to the output space \(Y=f(X)\). Therefore, it can be said that the ANN is a graph created by composing different transfer functions of individual neurons. Thus, the transfer function is a composite function that maps a vector from the N dimensional input space, also called the _parameter space_, to the neuron _output space_[25]. An artificial neuron whose inputs are assigned the weight factors, and whose output function is a simple step function or threshold function, is called a perceptron, and the problem it most often solves is a linear classification, meaning that data in the space must be linearly separable [26]. The main feature and advantage of the ANN is learning, which involves the process of adapting the network parameters [27]. The fact that MLP, in addition to the input and output layer of the ANN, has one or more hidden layers in its architecture that increase the depth of the network gives it the greatest advantage and power in this class of models because the output function of neurons is nonlinear, usually of the sigmoid shape [28]. According to [29], a multilayer perceptron is considered to allow nonlinear mapping between the input and the corresponding output vector. The learning rate, the number of hidden layers, the number of neurons in each hidden layer, and the shape of the output function of the hidden layer neurons are metaparameters of the model and are estimated before the start of the network training or learning. Therefore, an MLP is designed to approximate (universal approximator) any continuous function, and in addition, it can solve the problems that are not linearly separable.