# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 6 Summary and Future Trends
The hierarchical model, on the other hand, proved to be suitable for the treatment of complex structures with a particularly extensive dataset at the expense of a higher computational cost. A weakness of these methodologies lies in the difficulty of repeatable results. A well-labeled training dataset is resource-intensive. On the other hand, its availability makes it possible to use supervised classification techniques preferable to unsupervised techniques, at least in the case of complex structures. The unsupervised algorithms return the data of the training set that are in the vicinity of the data of the test sets, thus providing an estimate of the belonging of such data to a specific class. These algorithms are faster during the training process; however, they are slow during the grading process [111]. Algorithms based on clustering can also be very useful in identifying anomaly data, thus improving the quality of monitoring through AE sensors. Structural damage detection requires in-depth knowledge of the system, which is often not available. Often, monitoring the status of a system with the use of state-of-the-art sensors does not provide us with an exhaustive picture of its state of health. A robust damage identification system must fill these gaps through innovative methodologies. The data-driven approach can guarantee a solution to the problem if it provides us with a model capable of generalizing. In fact, the purpose of machine learning is to build an algorithm that can classify new inputs never seen during the learning phase. The generalization capacity of the system is precisely expressed in the ability to make correct predictions on inputs not observed during training. The effectiveness of a self-learning algorithm is measured with a low training error and a small difference between the training and test errors. These two factors identify two of the problems related to machine learning:overfitting and underfitting. Underfitting occurs when the model is unable to achieve a sufficiently small training error. Overfitting, on the other hand, occurs when the gap between training and test errors is too wide. There are many algorithms based on ML, and each family of algorithms has specific characteristics that govern their use in each context. Table 7 compares the performances returned by the models most adopted by the scientific community for AE testing. To facilitate comparison, ranges of values declared by the authors in the respective articles have been reported. Accuracy metric has been adopted for performance evaluation. Accuracy measures how close the forecast is to the current value; it is usually given as a percentage. AE and ANNs have been used in the literature to address the problem of localization of fracture sources. For the analysis of AE data, advanced algorithms based on pattern recognition have been developed with the use of ANNs. These methods require signal characteristics extracted in the time or frequency domain. These techniques do not require knowledge of the wave velocity or the structural geometry of the material and are able to estimate the intensity of the source and its position. An ANN shows itself to be efficient in the classification of faults; then, by adapting the structure of the network to the type of data, it is possible to significantly increase its classification capacity. However, the ANN training process is complex and computationally demanding. Analyzing Table 7, we can see that the accuracy returned by the models for fault identification has comparable values. This confirms that the evaluation metric, referable to the works available in the literature, is not the suitable tool to guide the researcher in the choice of the most appropriate algorithm for the identification of a specific structural damage. This choice can be made only after verifying how the different algorithms adapt to the available data, providing the system with adequate generalization capacity.