This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. Experimental results demonstrate the framework’s adaptability and high performance in the financial sector (accuracy: 94%, recall: 95%), healthcare (accuracy: 96%, precision: 93%), and smart city infrastructures (accuracy: 91%, F1 score: 90%). The framework achieves a balanced trade-off between accuracy (0.96) and computational efficiency (processing time: 1.5 s per transaction), making it ideal for real-time enterprise deployments. Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. Future work aims to enhance real-time scalability further and explore reinforcement learning to advance proactive threat mitigation measures. This research provides a scalable, adaptive, and practical solution for enterprise-level cybersecurity and data privacy management.

In the rapidly advancing landscape of artificial intelligence (AI), the intersection of privacy and security has emerged as a critical focal point. This chapter explores the multifaceted challenges and considerations involved in safeguarding personal data within the AI era. It delves into the ethical implications of AI-driven data collection, storage, and utilization, emphasizing the importance of privacy-preserving technologies and robust security measures. Through case studies and theoretical frameworks, the chapter examines current practices and future directions aimed at balancing innovation with the protection of individual privacy rights. By addressing these issues, it aims to equip stakeholders—from developers to policymakers—with the knowledge needed to navigate the complex terrain of AI ethics and ensure responsible data stewardship in the digital age.

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 study evaluates the factors influencing financial stability (FS) and behavioral intention (BI) in a cryptocurrency exchange app, explicitly focusing on system quality (SQ), perceived trust (PT), and digital currency (DC) within the Indonesian context. Utilizing structural equation modeling (SEM) with SmartPLS, the research analyzed data from 345 respondents who are active users of the cryptocurrency exchange app. The results confirmed that SQ significantly enhances PT (β = 0.832, t = 27.216, p < 0.001) and BI (β = 0.718, t = 12.675, p < 0.001). Additionally, DC positively impacts FS (β = 0.578, t = 8.177, p < 0.001), while PT influences both FS (β = 0.391, t = 5.478, p < 0.001) and BI (β = 0.198, t = 3.490, p = 0.001). These findings validate all five proposed hypotheses, highlighting the critical role of SQ and PT in driving FS and user engagement in cryptocurrency exchange apps. The study's measurement model demonstrated good reliability and validity, with Cronbach's alpha values exceeding 0.7 for all constructs: SQ (0.891), PT (0.812), DC (0.767), FS (0.819), and BI (0.745). Composite reliability values were also high, ranging from 0.855 to 0.933. Average Variance Extracted (AVE) values indicated good convergent validity, with SQ (0.822), PT (0.727), DC (0.689), FS (0.743), and BI (0.663). Discriminant validity was confirmed using the Fornell-Larcker criterion. The structural model's fit indices, including an SRMR of 0.045 and an NFI of 0.914, demonstrated a good model fit. The R² values for BI (0.791), FS (0.873), and PT (0.693) indicated substantial explanatory power. Despite its contributions, this study has limitations, including its focus on a single cryptocurrency exchange app in Indonesia, which may affect the generalizability of the findings. Future research should expand the sample to include multiple apps and geographical contexts. Additionally, incorporating other relevant factors, such as user experience and regulatory compliance, could provide a more comprehensive understanding of FS in digital financial services. This research underscores the importance of SQ and PT in achieving long-term success and sustainability in the rapidly evolving digital finance landscape.

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.