Abstract : This article explores the integration of neural networks into cybersecurity frameworks for distributed cloud systems. The study examines architectural requirements, deployment strategies, model robustness, and resilience to adversarial attacks. Particular attention is paid to real-time anomaly detection, scalability, and the challenges of maintaining model integrity across decentralized environments. The proposed approaches demonstrate the effectiveness of neural models in enhancing threat detection and adaptive defense mechanisms in complex cloud infrastructures.

Abstract : Deep learning techniques are increasingly used to detect malicious anomalies in IoT environments, where traditional security mechanisms fail to scale or adapt to dynamic data flows. This study evaluates various neural network architectures suitable for constrained devices, analyzes deployment models from edge to cloud, and examines the resilience of DL systems to adversarial threats. Practical implementation details, including preprocessing pipelines and lightweight inference, are presented alongside comparative performance metrics. Challenges related to data availability, explainability, and system heterogeneity are identified as critical barriers to widespread adoption.

Abstract : The article explores the stages in the evolution of web architectures within the context of industrial IT systems – from monolithic and centralized solutions to distributed models, including edge computing. It analyzes architectural and network transformations associated with the shift toward edge-based computation, as well as the impact of these changes on fault tolerance, processing latency, and node autonomy. It is emphasized that the implementation of edge infrastructures requires new approaches to containerization, communication protocols, and security. Special attention is given to the use of edge computing for inventory management, production processes, and integration with ERP/SCADA systems. Practical examples from high-precision manufacturing and logistics sectors are presented. The article underscores the need to rethink architectural principles in the era of industrial digitalization.

Abstract : This article investigates the application of quantum algorithms in the domain of big data analysis, focusing on their theoretical foundations, architectural integration, and sector-specific use cases. The study provides a comparative assessment of classical and quantum approaches to core analytic tasks such as search, optimization, and dimensionality reduction. Key attention is given to hybrid quantum–classical models, implementation challenges, system security, and operational reliability. The article concludes with an overview of current limitations and outlines prospective research directions that can guide the practical deployment of quantum-enhanced analytics in large-scale data environments.

Abstract : Digital identity built on distributed ledger technologies (DLT) is emerging as a foundational component of next-generation e-government infrastructures. Successful implementation depends on a combination of architectural design, regulatory frameworks, and technical interoperability. This paper presents a comparative assessment of global maturity levels in DLT-based identity systems and evaluates core platform characteristics. The relevance of self-sovereign identity (SSI) is emphasized as a means to enhance user control and privacy. A methodological approach is proposed for selecting technological architectures that align performance, scalability, and compliance objectives. The findings provide practical guidance for national digital transformation strategies and public sector implementation.

Abstract : The article analyzes the engineering and architectural foundations underpinning the development of visual editors for digital media. It examines principles of modularity, extensibility, and separation of concerns, which enable the creation of interactive content without programmer involvement. It is emphasized that such editors support the automation of multimedia content production, facilitate integration with websites via API, and ensure secure isolation of executable code. The article also examines monetization strategies that include integrating advertising formats into visual media and assesses the impact of interactive features on audience engagement metrics. It is emphasized that utilizing visual editors aids in streamlining resource usage and fostering a technology-resistant framework for digital media creation.

Abstract : This paper explores the architectural and operational complexities of implementing microservice-based architectures (MSAs) in financial platforms. It investigates key challenges related to modular service decomposition, inter-service communication, data consistency, and security enforcement, with particular focus on high-assurance environments. Emphasis is placed on hybrid design patterns, including event-driven coordination, fault isolation, and observability-driven scaling, which enable resilience and regulatory compliance. The analysis is supported by diagrams and tabular comparisons illustrating practical configurations. The findings aim to guide the development of scalable, auditable, and fault-tolerant financial systems capable of sustaining real-time operations in dynamic conditions.

Abstract : This paper investigates the application of graph databases for user behavior analysis, highlighting their advantages over relational models in representing and querying complex interaction patterns. Key aspects explored include data modeling techniques, query strategies using Cypher, integration into analytics pipelines, graph algorithm use cases, and system performance comparisons. Visual analyses and empirical benchmarks demonstrate the efficiency of graph-native operations in behavioral contexts, particularly for multi-hop queries and influence modeling. The study also addresses operational challenges and outlines emerging trends such as graph machine learning and temporal graph modeling. The results support the adoption of graph databases as a core component of intelligent, relationship-aware analytical systems.

Abstract : The article addresses the issue of neural network robustness against adversarial attacks in medical systems. It analyzes common architectures used in medical image classification and biosignal analysis in terms of their vulnerability to various types of attacks. Several robustness enhancement methods are presented, including adversarial training, distillation, and input preprocessing. The use of auxiliary models for attack detection is also discussed. The study highlights the importance of a comprehensive protection strategy for medical AI systems, considering both computational and clinical constraints.

Abstract : This paper investigates the application of fusion-based algorithms for predicting equipment failures in industrial environments. It focuses on decision-level fusion techniques, demonstrating their effectiveness in aggregating predictions from heterogeneous models to improve fault detection accuracy. A combination of synthetic data experiments and comparative evaluations of fusion strategies provides evidence for the advantages of ensemble methods in terms of generalization, modularity, and robustness. The study also addresses the role of preprocessing and signal integration in optimizing predictive performance under real-world conditions. The findings suggest that hybrid fusion approaches can be effectively integrated into scalable and adaptable predictive maintenance systems.