Phishing remains a persistent and evolving cyber threat, constantly adapting its tactics to bypass traditional security measures. The advent of Machine Learning (ML) and Neural Networks (NN) has significantly enhanced the capabilities of automated phishing detection systems. This comprehensive review systematically examines the landscape of ML- and NN-based approaches for identifying and mitigating phishing attacks. Our analysis, based on a rigorous search methodology, focuses on articles published between 2017 and 2024 across relevant subject areas in computer science and mathematics. We categorize existing research by phishing delivery channels, including websites, electronic mail, social networking, and malware. Furthermore, we delve into the specific machine learning models and techniques employed, such as various algorithms, classification and ensemble methods, neural network architectures (including deep learning), and feature engineering strategies. This review provides insights into the prevailing research trends, identifies key challenges, and highlights promising future directions in the application of machine learning and neural networks for robust phishing detection.

Today, there is a marked increase in interest in the share of renewable energy sources in the energy mix, which benefits the environment. This also applies to the energy of sea (wave) current, as, without a doubt, the offshore area is becoming one of the leading areas of renewable energy, which translates into changes in energy production. This can be exemplified by the latest research in the context of theory, design, modeling, as well as application, control, and monitoring of wave turbines to enhance their performance. This article reviews the research in this context, systematizes information, identifies literature gaps, and presents future directions in this area. For this purpose, 3240 English-language publications from 2015 to 2024 were identified in the Scopus database. The data are analyzed according to the selected research domains. Some of them are review or conceptual in nature, while others are empirical in nature (experimental attempts and case studies). From this point of view, it becomes possible not only to systematize the state-of-the-art but also to identify future research prospects.

This review paper analyzes the recent applications of computational mechanics methods in combination with machine learning (ML) and neural network (NN) techniques, as found in the literature published between 2015 and 2024. We present how ML and NNs are enhancing traditional computational methods, such as the finite element method, enabling the solution of complex problems in material modeling, surrogate modeling, inverse analysis, and uncertainty quantification. We categorize current research by considering the specific computational mechanics tasks and the employed ML/NN architectures. Furthermore, we discuss the current challenges, development opportunities, and future directions of this dynamically evolving interdisciplinary field, highlighting the potential of data-driven approaches to transform the modeling and simulation of mechanical systems. The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics, and physics-informed/operator learning. Accordingly, the timespan is now 2015–2025, with a focused inclusion of high-impact contributions from 2024 to 2025.

This paper presents information on the propagation patterns of acoustic waves and their practical application, in particular, in modern fire detection methods that use artificial vision systems and video cameras as intelligent sensors. In practice, the use of artificial intelligence allows the detection of flames in places where the use of typical sensors is impossible or severely limited. Such a system can work together with environmentally friendly acoustic flame extinguishing technology as a standalone platform, or it may cooperate with other modules, which is a new approach in the field of fire protection. An analysis shows that the presented eco-friendly methods outperform other methods, with many advantages. In the future, the acoustic method can be used for the monitoring and early detection of fires in factory buildings or objects of high cultural, religious, and historical value, while an acoustic extinguisher equipped with artificial vision systems can be successfully used to extinguish fires.

The increasing demands for the reliability of modern industrial equipment and structures necessitate advanced techniques for design, monitoring, and analysis. This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. A total of 96 peer-reviewed scientific publications were examined, selected using a systematic Scopus-based search. The main research areas include processes such as modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. These techniques were applied across a wide range of engineering domains, including civil infrastructure, transportation systems, energy installations, and rotating machinery. Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. The findings indicate a clear shift towards deep learning-based methods and multisensor data fusion, accompanied by increasing use of automatic feature extraction and interest in transfer learning, few-shot learning, and unsupervised approaches. This review aims to provide a comprehensive understanding of the current state and future directions of machine learning applications in vibration and acoustics, outlining the field’s evolution and identifying its key research challenges and innovation trajectories.