Electroencephalography (EEG) is a widely used non-invasive method for capturing brain activity, offering valuable insights into cognitive and emotional states relevant to mental health. With the growing complexity and volume of EEG data, machine learning (ML) techniques—particularly deep learning—have become integral in extracting meaningful patterns. While much of the current literature focuses on supervised learning methods that rely on labeled data, unsupervised learning offers an alternative approach capable of discovering hidden structures and novel biomarkers without requiring predefined labels. This systematic review aimed to identify and synthesize recent peer-reviewed research that applied unsupervised or self-supervised learning techniques to EEG data in the context of mental health monitoring, diagnosis, or analysis. A comprehensive search was conducted across six major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, and Google Scholar, covering literature from January 2018 to March 2025. Following PRISMA guidelines, predefined inclusion and exclusion criteria were applied to screen and assess the relevance and quality of studies. From 512 initial records, 403 unique articles were screened, and 20 underwent full-text review. Ultimately, no studies met all the inclusion criteria. Most were excluded for employing only supervised methods, being review articles, or focusing on non-mental-health applications. The absence of eligible studies highlights a significant gap in current research and emphasizes the need for future empirical work exploring unsupervised techniques in EEG-based mental health applications. Such efforts could pave the way for more scalable, label-free approaches to understanding brain dynamics in psychological conditions.

This Grounded Theory examines the in-depth dynamics of cybersecurity professionals working in financial organizations regarding stress management, risk handling, communication challenges, and perceived benefits from work. This study conducted a grounded theory approach with cybersecurity professionals through in-depth interviews to comprehensively understand these dynamics. The findings identify critical themes about emotional and psychological resilience, effective risk management strategies, overcoming communication challenges, and recognizing organizational and personal benefits from robust cybersecurity practices. The current research highlights that for better well-being and performance of cybersecurity teams, extensive support systems, proactive risk management, and communication are very important and should be embedded within organizations. By utilizing the grounded theory, this study offers a nuanced exploration of the complex and entwined factors that impact daily operations and the overall effectiveness of cybersecurity professionals, offering valuable insights for improving organizational policies and creating a supportive work environment. These findings suggest, most importantly, that robust support systems, risk assessments, clear communication protocols, and training opportunities should be developed and operationalized in financial organizations to help professionals reduce stress at work and strengthen resilience factors. Not only does this research project cover significant gaps in the literature, but it also really helps financial organizations further improve their currently existing cybersecurity strategies to better support personnel by using this framework.

This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving the accuracy and lead times of tsunami occurrence predictions. The study employs machine learning methods, including Random Forest and Logistic Regression, for binary classification of tsunami events. Data collection is performed using a Kaggle dataset spanning 1995–2023, with preprocessing and exploratory analysis to identify critical patterns. The Random Forest model achieved superior performance with an accuracy of 0.90 and precision of 0.88 compared to Logistic Regression (accuracy: 0.89, precision: 0.87). These results underscore Random Forest’s effectiveness in handling imbalanced data. Challenges such as improving data quality and model interpretability are discussed, with recommendations for future improvements in real-time warning systems.

The rapid integration of artificial intelligence (AI) into software development, driven by large language models (LLMs), is reshaping the role of programmers from traditional coders into strategic collaborators within Industry 4.0 ecosystems. This qualitative study employs a hermeneutic phenomenological approach to explore the lived experiences of Information Technology (IT) professionals as they navigate a dynamic technological landscape marked by intelligent automation, shifting professional identities, and emerging ethical concerns. Findings indicate that developers are actively adapting to AI-augmented environments by engaging in continuous upskilling, prompt engineering, interdisciplinary collaboration, and heightened ethical awareness. However, participants also voiced growing concerns about the reliability and security of AI-generated code, noting that these tools can introduce hidden vulnerabilities and reduce critical engagement due to automation bias. Many described instances of flawed logic, insecure patterns, or syntactically correct but contextually inappropriate suggestions, underscoring the need for rigorous human oversight. Additionally, the study reveals anxieties around job displacement and the gradual erosion of fundamental coding skills, particularly in environments where AI tools dominate routine development tasks. These findings highlight an urgent need for educational reforms, industry standards, and organizational policies that prioritize both technical robustness and the preservation of human expertise. As AI becomes increasingly embedded in software engineering workflows, this research offers timely insights into how developers and organizations can responsibly integrate intelligent systems to promote accountability, resilience, and innovation across the software development lifecycle.

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs)...

This study explores the analysis of EEG signal data for real-time mental health monitoring using advanced unsupervised deep learning models. Employing algorithms such as autoencoders, Principal Component Analysis (PCA), K-means clustering, and Gaussian Mixture Models (GMM), this research aims to uncover patterns and biomarkers indicative of various mental health conditions. The study utilizes a comprehensive dataset comprising EEG signals from different brain regions, focusing on the extraction of significant features and the training of models to detect subtle yet crucial changes in brain activity. Our findings demonstrate enhanced capability for early detection of mental health issues, with improved predictive accuracy and potential for personalized therapy, underscoring a promising future for mental health care. Furthermore, the study rigorously addresses the ethical implications of using algorithmic approaches in healthcare, such as potential biases, patient privacy, and the welfare of individuals. By implementing these unsupervised deep learning models, our research offers compelling opportunities for the prevention, tailored intervention, and improved treatment outcomes in mental health care while also emphasizing the importance of navigating the ethical complexities to ensure responsible technology deployment for enhancing patient well-being and safety.