Abstract Aluminum (Al) and magnesium (Mg) matrix composites are attractive materials for automotive, aerospace, and energy applications due to their low density, high specific strength, and favorable wear performance. However, despite extensive experimental efforts, predicting their mechanical, tribological, and corrosion behaviors remains challenging because of the complex and nonlinear interactions among composition, processing parameters, and reinforcement characteristics. To address this need, this review provides a structured synthesis of recent machine learning (ML) applications to Al and Mg matrix composites, focusing on studies published between 2021 and 2025. A systematic multidatabase search was conducted, and the selected works were examined within a unified ML pipeline encompassing data acquisition, preprocessing practices, feature handling, learning strategies, training protocols, and performance evaluation. The analysis reveals trends in dataset size, target properties, descriptor types, and modeling choices. It also highlights recurring challenges related to data scarcity, heterogeneous reporting of preprocessing and evaluation practices, and uneven coverage of different property classes. Informed by these findings, the review provides practical recommendations for data curation, preprocessing, model selection, and training to facilitate more reliable ML-based prediction and data driven guidance for improving the properties of Al and Mg matrix composites.

To decode the motion intention from the surface electromyography signals (sEMG) for designing multi-functional prosthetic devices, machine learning (ML) and deep learning (DL) techniques have been widely adopted. Nevertheless, the complexity of the prosthetic hand and the non-stationary characteristics of sEMG signals introduce several practical challenges in adopting ML/DL techniques in the myoelectric prosthetic design. To this end, considerable research attention has been paid to enhance the model reliability, adaptation and robustness. In this article, we present a comprehensive review on the latest advancements in sensing modalities in prosthetics, publicly available datasets, prominent features for classifier training and the feature selection techniques used in the classifier model. Specifically, this article presents a survey on ML and DL techniques for myoelectric hand prosthetic control spanning nearly two decades (2007–2024) by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and using Web of Science and PubMed databases. This scoping review also elucidates the potentials of different sensing modalities in myoelectric control other than sEMG signal for enhancing the robustness and reliability of the prosthetic device. Moreover, open research challenges and emerging research directions in terms of hardware design, multi-modal sensing, and motion decoding techniques are also critically analyzed to provide insights on future developments.