machine learning

Detection and identification of inclusions in the modeling of stationary processes is a crucial task in many technical fields, including materials science, electronics, and non-destructive testing. The presence of inclusions can affect the mechanical, thermal, and electrical properties of a material, making the accurate determination of their geometric and physical characteristics essential. The use of modern numerical methods and deep learning techniques opens new opportunities for improving the efficiency and accuracy of prediction results.

The rapid accumulation of information assets requires new approaches to their storage and protection. The article is devoted to the analysis of modern approaches to storing large volumes of data, taking into account their efficiency, reliability and security. Key technologies such as cloud platforms, local solutions and distributed storage systems are considered, as well as the features of their application.

The paper analyzes the role of pseudorandom number generators in ensuring the efficiency and stability of machine learning algorithms. The key areas and forms of such application are identified.

In the article, modern approaches to the development of Intelligent Transportation Systems (ITS) aimed at optimizing urban traffic are analyzed. Special attention is paid to model-free reinforcement learning algorithms (Q-Learning and Deep Q-Learning) used for controlling traffic lights in dynamic road traffic conditions. Simulation results in the SUMO environment have proven that implementing such algorithms significantly reduces intersection queues and increases the capacity of the transportation network.

In the current context of digital transformation within the energy drinks market, the use of digital technologies has become a crucial tool for enhancing the efficiency of business processes, marketing strat- egies, and consumer engagement. However, despite considerable opportunities, the widespread imple- mentation of digital instruments in this sector faces several challenges that require both academic analysis and practical solutions. One of the key issues is the adaptation of energy drink producers' business models to the realities of the digital environment.

The article proposes an innovative approach to optimize the routing process in distributed networks using machine learning techniques, specifically reinforcement learning. This method enables the adaptive determination of optimal data transmission paths based on current network conditions, enhancing overall performance and resilience to dynamic traffic fluctuations. The proposed approach dynamically adjusts to variations in network topology, traffic load, and node availability, ensuring efficient data flow management even in highly dynamic environments.

The article examines modern methods of applying machine learning and recommendation systems for sentiment analysis of users in information and communication environments. Social networks and digital platforms have become important sources of public opinion, generating large volumes of textual data daily. Traditional analysis methods, such as lexical approaches or classical machine learning algorithms, have limitations in detecting context, sarcasm, slang, and emotional nuances in the text. This complicates the accurate identification of user emotions and socially significant topics.

The classification of Border Gateway Protocol (BGP) anomalies is essential for maintaining Internet stability and security, as such anomalies can impair network functionality and reliability. Previous studies has examined the impact of key features on anomaly detection; however, current methodologies frequently demonstrate high computational costs, complexity, and usage challenges.

The increasing complexity of cyber threats requires the development of effective methods for detecting and classifying attacks in network traffic. This study analyzes the effectiveness of three popular machine learning algorithms: Random Forest, which is used for anomaly detection, Support Vector Machines (SVM), which performs cyber threat classification, and autoencoders, which are used for data preprocessing and deep traffic analysis.