This review identifies current machine-learning algorithms implemented in building structural health monitoring systems and their success in determining the level of damage in a hierarchical classification. The integration of physical models, feature extraction techniques, uncertainty management, parameter estimation, and finite element model analysis are used to implement data-driven model detection systems for SHM system design. A total of 68 articles using ANN, CNN and SVM, in combination with preprocessing techniques, were analyzed corresponding to the period 2011–2022. The application of these techniques in structural condition monitoring improves the reliability and performance of these systems.

In recent years the Android Operating System (OS) has become one of the major stakeholders in the smartphone market. The growing consumers' adoption of Android has also brought many security concerns as the number of malicious applications targeting this OS has dramatically increased. Current malware detection methods include static and dynamic analysis. In this work, a set of results obtained for malware classification through machine learning techniques are presented. Although, the presented approach analyzes data obtained through static analysis techniques as other approaches, it differs from previous works by presenting detailed descriptions of the data sets characteristics, feature extraction and selection processes, the size of the training sample set, cross validation and validation sets are specified, providing explicit evidence for classification improvement. Even more, a comparative analysis of various ensembles is presented, having as objective to determine the best combination of classifiers based on the evaluation of the classification results.