Abstract : Adaptive machine learning algorithms are becoming increasingly relevant in the context of processing large volumes of streaming data that are constantly updated and contain significant amounts of noise and outliers. The purpose of this article is to analyze the potential of adaptive algorithms for real-time streaming data processing, with a focus on their application in areas such as financial analytics, the Internet of Things, and cybersecurity. Key methods are discussed, including recurrent neural networks, stochastic gradient descent, and the least squares method, along with their advantages and limitations. Special attention is given to anomaly detection and error prevention using regularization and ensemble methods. The presented results highlight the importance of adaptive algorithms for improving analytical accuracy and system resilience in dynamic environments.

Abstract : This article examines methods for integrating machine learning (ML) into big data management systems to enhance analytical capabilities and optimize data processing. It analyzes algorithms such as decision trees and deep neural networks, which are applied in tasks like data segmentation, clustering, and time series analysis. Special emphasis is placed on forecasting based on historical data, allowing for improved adaptability and accuracy in analytical systems. Challenges related to computational resources and model robustness against noise and changing data are discussed, with proposed solutions. The results highlight the role of ML in enhancing the efficiency and reliability of modern big data management systems.

Abstract : The article analyzes decentralized authentication methods used in distributed networks to enhance system security and fault tolerance. Cryptographic approaches, including asymmetric encryption and blockchain, are reviewed as means to protect data and prevent unauthorized access. The focus is placed on the scalability and performance of decentralized solutions, as well as their potential in Internet of Things networks and other high-load systems. This analysis highlights both the strengths and limitations of these approaches, offering insights into their viability within evolving network demands.

Abstract : The article examines methods for improving blockchain network reliability under high load conditions. Examples of consensus algorithms such as Proof-of-Work, Proof-of-Stake, and hybrid models in Bitcoin, Ethereum, and Solana networks are presented. Alternative solutions, including sharding and second-layer protocols, are discussed to enhance network scalability and response times. Special attention is given to methods that reduce energy consumption and increase resistance to attacks. The presented analysis emphasizes the importance of algorithm selection and scalability approaches to ensure the stability and security of blockchain networks under heavy load.

Abstract : This article focuses on the analysis of consumer behavior patterns using Big Data technologies and machine learning (ML) methods. It describes clustering and classification algorithms, including K-means and Random Forest, for audience segmentation and user behavior prediction. A frequency analysis of data distribution is conducted to identify anomalies and outliers, which is essential for accurate forecasting models. The significance of various features in the analysis, such as visit frequency and time spent on pages, is highlighted. The results emphasize the importance of modern analytical tools to enhance competitiveness and adapt business strategies. The presented methods allow for a deeper understanding of consumer behavior and improved personalization of marketing offers. The article discusses future research directions to further improve analysis efficiency.

Abstract : The article discusses the main approaches to storing unstructured data using NoSQL databases, including document-oriented databases (e.g., MongoDB) and key-value stores (e.g., Redis). Examples of practical applications of both technologies are provided, along with code snippets and explanations. The advantages of MongoDB, such as data structure flexibility and horizontal scaling, are highlighted, as well as the benefits of Redis, including high-speed access due to in-memory data storage and support for various data structures. Key differences between these databases and their suitability for different use cases are analyzed. The article emphasizes the importance of choosing the right database according to system requirements. Future research prospects involve integrating NoSQL solutions and creating hybrid architectures to enhance data storage and processing efficiency.

Abstract : This article examines methods and technologies aimed at reducing energy consumption in Internet of Things (IoT) devices. Examples of energy-efficient communication protocols such as BLE, Zigbee, and LoRaWAN are described, along with their characteristics and primary applications. The use of low-power modes for microcontrollers and software optimization techniques to reduce energy expenditure is analyzed. Special attention is given to adaptive frequency management algorithms and event-driven architectures, enabling devices to use resources efficiently. Recommendations are provided for selecting methods suitable for various IoT devices based on their specific tasks. The article emphasizes the importance of a comprehensive approach to improving energy efficiency and identifies future research directions for enhancing current technologies.

Abstract : The article discusses strategies for unit testing used to validate composite applications. It describes tools such as JUnit, PyTest, NUnit, Jest, and Mocha, each with unique features and benefits. Special attention is given to Mocha, a tool for testing JavaScript applications, including asynchronous operations. Practical examples of using Mocha for testing modules and APIs with support for libraries such as supertest are provided. Approaches to writing tests using assertions and functional methods are reviewed, highlighting how to automate the testing process and improve code quality. The article emphasizes the importance of unit testing in modern projects and the need for selecting appropriate tools to ensure application stability and reliability.

Abstract : This article discusses data protection methods for distributed cloud systems (DCS), including symmetric, asymmetric, and hybrid encryption, multi-factor authentication, and distributed access control systems. Key characteristics of encryption methods are presented, along with an analysis of their advantages and limitations. Notable data breaches in major companies such as Capital One, Facebook, and Uber are described, emphasizing the importance of robust security measures and regular audits. Modern data protection technologies and their applications in the context of increasing cybersecurity threats are explored. The article highlights the importance of a comprehensive approach and the integration of new methods to enhance the reliability of cloud systems.