### Selection Method of Analyzed Articles Based on PRISMA Methodologies
Figure 4: Diagrams related to the publishers of identified documents (**a**) and scientific articles’ typology (**b**). In many smart manufacturing contexts, the use of ML algorithms has contributed notable advantages, such as the possibility of automating some processes that would normally require human intervention. For example, PdM combats maintenance schedules that are not always accurate by using ML algorithms to identify a potential problem before a failure occurs [35]. Inspection, monitoring, and process control are other areas where ML has had considerable success, acquiring information from cameras and sensors and promoting accurate and rapid inspection of parts. The ML algorithms are classified into four different types: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning (Figure 5) [36]. Supervised learning: This algorithm trains itself using a labeled dataset where each input datum corresponds to a well-defined output. By looking at these labeled examples, the algorithm can transfer the input attributes to the intended output [37; 38; 39]. The classification is to classify or enumerate the data into predetermined classes or categories. Generally, the output is a discrete value representing a class or a category. Supervised MLs include classification and regression algorithms. Classification algorithms predict a result as a discrete value indicating the object's membership in a specific class. Regression algorithms, on the other hand, differ from classification in that the results are expressed through continuous values. This classification of machine learning algorithms includes regression algorithms, logistic regression, Naive Bayes, K-nearest neighbors (KNN), decision trees, random forest, and XGBoost [40; 41; 42; 43; 44]. Unsupervised learning: This is the process of extracting patterns or structures from data without knowing the outcome. The method is used for an unlabeled data collection to learn an input representation that captures the underlying data structure. Unsupervised learning is divided into clustering, dimensionality reduction, density estimation, and anomaly detection [38; 45]. Clustering is grouping similar data points based on specific characteristics or similarities. Dimensionality-reduction techniques aim to reduce the number of features in a dataset while preserving important information. Density estimation involves estimating the probability density function of a dataset. Anomaly detection identifies data points that deviate significantly from expected or normal behavior [46]. Examples of unsupervised learning algorithms are the staked auto-encoder algorithm (SAE) [47], k-means, k-medoids, and fuzzy c-means [38]. Semi-supervised learning: Semi-supervised learning combines supervised and unsupervised learning methods, using labeled and unlabeled data to improve model performance [38]. The main categories of this approach are as follows:  
* Self-training: an algorithm that trains a model on labeled data, then uses the model to label unlabeled instances, integrating them into the labeled dataset and repeating the process. * Graph-based methods: Data points are represented as nodes in a graph, propagating labels through it to label unlabeled nodes. Figure 5: Classification of the machine learning algorithms. * Co-training: Multiple models are trained on different subsets of the data, exchanging labels for unlabeled instances. * Multi-view learning: Data are analyzed from different perspectives, and models work together to optimize overall performance. * Reinforcement learning: This type of ML consists of training an agent through the feedback obtained from its responses on the application environment [38]. This agent is assigned rewards and penalties for the activities performed to define a policy that allows the maximum reward to be assigned [43]. ML is a process by which a computer system is trained to make accurate predictions using new data. Training, learning, and evaluation are the three basic steps in building an ML model.