### Selection Method of Analyzed Articles Based on PRISMA Methodologies
The model's accuracy depends on the quality of the initial data and the algorithm's performance. The first step is to acquire input and output data, i.e., features (input) and labels (output). The second data step is to train the model using the data from the previous step. In the third step, the performance is evaluated once the algorithm training is completed. For this performance evaluation, loss functions are used to observe how much the predicted value diverges from the real value. Two loss functions are used mainly: the mean absolute error (MAE) and the mean square error (MSE) [48]. Figure 6 schematically shows the operating phases of an ML algorithm. The main ML algorithms used in smart manufacturing are described as follows:  
* Regression algorithms: These predict a numerical value based on other input variables. They are used in many predictive analytics applications, such as stock price forecasting in finance, weather forecasting, and market demand assessment. Linear regression is the simplest type, as it aims to predict a numerical value based on a single input variable. The algorithm generates a line representing the relationship between the input variable and the predicted numerical value. * Logistic regression: This is more complex than the linear variant and is used to predict the value of a binary variable based on other input variables [49]. The algorithm generates an \"S\" curve that describes the probability of the output value based on the input variable. Among the possible applications, for example, is that logistic regression can predict if a patient will develop a disease based on documented risk factors (input data). * Naive Bayes: This is an algorithm based on [PERSON] theorem, which assumes that the presence or absence of a particular feature in a document is not related to the presence or absence of other features [49]. The operation of the Naive Bayes algorithm is based on estimating the conditional probabilities of the independent variables given the dependent variables. Simple and fast to execute, this algorithm can, however, suffer from accuracy problems. * K-nearest neighbors (KNN): This is a classification algorithm based on the characteristics of objects close to the one being classified [49]. In other words, the KNN  
Figure 6: Operating scheme of a machine learning algorithm. The first phase involves acquiring testing and training data, followed by the algorithm training phase, and finally, evaluating the algorithm’s performance. algorithm classifies an object based on its proximity to other known objects. The operation of the KNN algorithm is based on the estimation of the distance between the characteristics of the object to be classified and those of the objects already known to the system. The KNN algorithm, therefore, uses the k objects closest to the object to be classified to determine its class. The choice of the value of k is made through various heuristic techniques. Decision tree algorithm: This is a graphical representation of a set of decision rules and their consequences [49]. Each internal node of the tree represents a variable, while each arc starting from an internal node represents a possible value for that variable. Finally, each leaf of the tree represents the predicted value for the target variable, starting from the values of the other properties. Decision trees are also used in more complex algorithms involving their combination. Random forest: This is a supervised learning algorithm that uses an ensemble technique to improve the accuracy and stability of the model [44]. It is used for classification and regression problems to predict a numerical value or a class based on one or more input variables. The operation of the random forest algorithm involves creating a set of decision trees, each of which is trained on a random subset of the data. This model generates a native independence between the trees, which results in a series of uncorrelated predictions, except in the final combination.